Overview

Dataset statistics

Number of variables113
Number of observations37606
Missing cells0
Missing cells (%)0.0%
Duplicate rows3724
Duplicate rows (%)9.9%
Total size in memory32.4 MiB
Average record size in memory904.0 B

Variable types

Numeric16
Categorical97

Alerts

Dataset has 3724 (9.9%) duplicate rowsDuplicates
bodystyle_Coupe is highly overall correlated with drivetrain_Rear-wheel DriveHigh correlation
bodystyle_SUV is highly overall correlated with bodystyle_Sedan and 1 other fieldsHigh correlation
bodystyle_Sedan is highly overall correlated with bodystyle_SUVHigh correlation
cat_x0 is highly overall correlated with cat_x1 and 1 other fieldsHigh correlation
cat_x1 is highly overall correlated with bodystyle_SUV and 1 other fieldsHigh correlation
cat_x2 is highly overall correlated with drivetrain_Rear-wheel DriveHigh correlation
drivetrain_All-wheel Drive is highly overall correlated with drivetrain_Front-wheel DriveHigh correlation
drivetrain_Front-wheel Drive is highly overall correlated with drivetrain_All-wheel Drive and 1 other fieldsHigh correlation
drivetrain_Rear-wheel Drive is highly overall correlated with bodystyle_Coupe and 2 other fieldsHigh correlation
exterior_color_x0 is highly overall correlated with exterior_color_x2High correlation
exterior_color_x2 is highly overall correlated with exterior_color_x0 and 1 other fieldsHigh correlation
exterior_color_x4 is highly overall correlated with exterior_color_x2High correlation
fuel_type_Electric is highly overall correlated with fuel_type_GasolineHigh correlation
fuel_type_Gasoline is highly overall correlated with fuel_type_Electric and 1 other fieldsHigh correlation
fuel_type_Hybrid is highly overall correlated with fuel_type_GasolineHigh correlation
interior_color_x0 is highly overall correlated with interior_color_x1 and 1 other fieldsHigh correlation
interior_color_x1 is highly overall correlated with interior_color_x0 and 1 other fieldsHigh correlation
interior_color_x2 is highly overall correlated with interior_color_x0 and 1 other fieldsHigh correlation
interior_color_x3 is highly overall correlated with interior_color_x4 and 1 other fieldsHigh correlation
interior_color_x4 is highly overall correlated with interior_color_x3High correlation
make_Acura is highly overall correlated with model_hashed_27High correlation
make_Dodge is highly overall correlated with model_hashed_12High correlation
make_Mazda is highly overall correlated with model_hashed_43High correlation
make_Nissan is highly overall correlated with interior_color_x3High correlation
make_RAM is highly overall correlated with model_hashed_30High correlation
make_Subaru is highly overall correlated with model_hashed_22High correlation
mileage is highly overall correlated with yearHigh correlation
model_hashed_12 is highly overall correlated with make_DodgeHigh correlation
model_hashed_22 is highly overall correlated with make_SubaruHigh correlation
model_hashed_27 is highly overall correlated with make_AcuraHigh correlation
model_hashed_30 is highly overall correlated with make_RAMHigh correlation
model_hashed_43 is highly overall correlated with make_MazdaHigh correlation
msrp is highly overall correlated with drivetrain_Front-wheel DriveHigh correlation
stock_type is highly overall correlated with yearHigh correlation
year is highly overall correlated with mileage and 1 other fieldsHigh correlation
model_hashed_0 is highly imbalanced (86.6%)Imbalance
model_hashed_1 is highly imbalanced (94.0%)Imbalance
model_hashed_2 is highly imbalanced (93.0%)Imbalance
model_hashed_3 is highly imbalanced (89.3%)Imbalance
model_hashed_4 is highly imbalanced (95.2%)Imbalance
model_hashed_5 is highly imbalanced (92.1%)Imbalance
model_hashed_6 is highly imbalanced (94.7%)Imbalance
model_hashed_7 is highly imbalanced (85.6%)Imbalance
model_hashed_8 is highly imbalanced (92.7%)Imbalance
model_hashed_9 is highly imbalanced (88.4%)Imbalance
model_hashed_10 is highly imbalanced (92.1%)Imbalance
model_hashed_11 is highly imbalanced (95.5%)Imbalance
model_hashed_12 is highly imbalanced (82.9%)Imbalance
model_hashed_13 is highly imbalanced (81.8%)Imbalance
model_hashed_14 is highly imbalanced (86.9%)Imbalance
model_hashed_15 is highly imbalanced (95.6%)Imbalance
model_hashed_16 is highly imbalanced (91.2%)Imbalance
model_hashed_17 is highly imbalanced (89.5%)Imbalance
model_hashed_18 is highly imbalanced (90.7%)Imbalance
model_hashed_19 is highly imbalanced (88.3%)Imbalance
model_hashed_20 is highly imbalanced (82.4%)Imbalance
model_hashed_21 is highly imbalanced (93.1%)Imbalance
model_hashed_22 is highly imbalanced (88.0%)Imbalance
model_hashed_23 is highly imbalanced (87.9%)Imbalance
model_hashed_24 is highly imbalanced (91.9%)Imbalance
model_hashed_25 is highly imbalanced (92.6%)Imbalance
model_hashed_26 is highly imbalanced (94.4%)Imbalance
model_hashed_27 is highly imbalanced (82.4%)Imbalance
model_hashed_28 is highly imbalanced (88.5%)Imbalance
model_hashed_29 is highly imbalanced (83.5%)Imbalance
model_hashed_30 is highly imbalanced (87.7%)Imbalance
model_hashed_31 is highly imbalanced (94.8%)Imbalance
model_hashed_32 is highly imbalanced (90.2%)Imbalance
model_hashed_33 is highly imbalanced (90.6%)Imbalance
model_hashed_34 is highly imbalanced (91.4%)Imbalance
model_hashed_35 is highly imbalanced (95.1%)Imbalance
model_hashed_36 is highly imbalanced (97.0%)Imbalance
model_hashed_37 is highly imbalanced (86.7%)Imbalance
model_hashed_38 is highly imbalanced (92.3%)Imbalance
model_hashed_39 is highly imbalanced (92.1%)Imbalance
model_hashed_40 is highly imbalanced (87.6%)Imbalance
model_hashed_41 is highly imbalanced (95.2%)Imbalance
model_hashed_42 is highly imbalanced (95.2%)Imbalance
model_hashed_43 is highly imbalanced (93.1%)Imbalance
model_hashed_44 is highly imbalanced (97.1%)Imbalance
model_hashed_45 is highly imbalanced (92.1%)Imbalance
model_hashed_46 is highly imbalanced (92.0%)Imbalance
model_hashed_47 is highly imbalanced (95.0%)Imbalance
model_hashed_48 is highly imbalanced (79.0%)Imbalance
model_hashed_49 is highly imbalanced (94.7%)Imbalance
model_hashed_50 is highly imbalanced (92.4%)Imbalance
model_hashed_51 is highly imbalanced (87.2%)Imbalance
drivetrain_Rear-wheel Drive is highly imbalanced (74.8%)Imbalance
make_Acura is highly imbalanced (85.7%)Imbalance
make_Audi is highly imbalanced (79.0%)Imbalance
make_BMW is highly imbalanced (71.6%)Imbalance
make_Buick is highly imbalanced (90.4%)Imbalance
make_Cadillac is highly imbalanced (79.7%)Imbalance
make_Chevrolet is highly imbalanced (53.5%)Imbalance
make_Dodge is highly imbalanced (81.3%)Imbalance
make_Ford is highly imbalanced (55.7%)Imbalance
make_GMC is highly imbalanced (84.3%)Imbalance
make_Honda is highly imbalanced (78.1%)Imbalance
make_Hyundai is highly imbalanced (65.5%)Imbalance
make_INFINITI is highly imbalanced (88.5%)Imbalance
make_Jeep is highly imbalanced (60.0%)Imbalance
make_Kia is highly imbalanced (80.1%)Imbalance
make_Land Rover is highly imbalanced (90.3%)Imbalance
make_Lexus is highly imbalanced (85.6%)Imbalance
make_Lincoln is highly imbalanced (86.4%)Imbalance
make_Mazda is highly imbalanced (78.9%)Imbalance
make_Mercedes-Benz is highly imbalanced (65.1%)Imbalance
make_Nissan is highly imbalanced (64.7%)Imbalance
make_Porsche is highly imbalanced (95.2%)Imbalance
make_RAM is highly imbalanced (88.2%)Imbalance
make_Subaru is highly imbalanced (74.0%)Imbalance
make_Toyota is highly imbalanced (78.3%)Imbalance
make_Volkswagen is highly imbalanced (67.8%)Imbalance
make_Volvo is highly imbalanced (92.4%)Imbalance
bodystyle_Cargo Van is highly imbalanced (91.9%)Imbalance
bodystyle_Convertible is highly imbalanced (93.9%)Imbalance
bodystyle_Coupe is highly imbalanced (81.6%)Imbalance
bodystyle_Hatchback is highly imbalanced (91.8%)Imbalance
bodystyle_Minivan is highly imbalanced (98.9%)Imbalance
bodystyle_Passenger Van is highly imbalanced (96.2%)Imbalance
bodystyle_Pickup Truck is highly imbalanced (58.2%)Imbalance
bodystyle_Wagon is highly imbalanced (96.7%)Imbalance
bodystyle_nan is highly imbalanced (97.2%)Imbalance
fuel_type_Electric is highly imbalanced (78.1%)Imbalance
fuel_type_Flexible is highly imbalanced (96.5%)Imbalance
fuel_type_Gasoline is highly imbalanced (61.7%)Imbalance
fuel_type_Hybrid is highly imbalanced (77.6%)Imbalance
mileage has 1543 (4.1%) zerosZeros

Reproduction

Analysis started2024-05-20 05:00:29.112146
Analysis finished2024-05-20 05:01:55.974551
Duration1 minute and 26.86 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

msrp
Real number (ℝ)

HIGH CORRELATION 

Distinct22059
Distinct (%)58.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46394.021
Minimum5991
Maximum270710
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size293.9 KiB
2024-05-20T00:01:56.083084image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum5991
5-th percentile21768.458
Q131356.101
median42285.5
Q355868.99
95-th percentile85523.217
Maximum270710
Range264719
Interquartile range (IQR)24512.889

Descriptive statistics

Standard deviation22072.061
Coefficient of variation (CV)0.47575228
Kurtosis7.1422003
Mean46394.021
Median Absolute Deviation (MAD)12040.5
Skewness1.8332037
Sum1.7446936 × 109
Variance4.8717589 × 108
MonotonicityNot monotonic
2024-05-20T00:01:56.268687image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34085 62
 
0.2%
54595 60
 
0.2%
36926 49
 
0.1%
35975 48
 
0.1%
33160 47
 
0.1%
28331 42
 
0.1%
32189 42
 
0.1%
48950 41
 
0.1%
33389 38
 
0.1%
32005 36
 
0.1%
Other values (22049) 37141
98.8%
ValueCountFrequency (%)
5991 1
< 0.1%
5995 1
< 0.1%
6000 1
< 0.1%
6188 1
< 0.1%
6649.48 1
< 0.1%
6679.57 1
< 0.1%
6688.68 1
< 0.1%
6783.84 1
< 0.1%
6888.45 1
< 0.1%
6929.44 1
< 0.1%
ValueCountFrequency (%)
270710 1
< 0.1%
254160 1
< 0.1%
251160 1
< 0.1%
249320 1
< 0.1%
242742 1
< 0.1%
238805 1
< 0.1%
229950 2
< 0.1%
227565 1
< 0.1%
226420 1
< 0.1%
225550 1
< 0.1%

year
Real number (ℝ)

HIGH CORRELATION 

Distinct47
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2021.7396
Minimum1961
Maximum2025
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size293.9 KiB
2024-05-20T00:01:56.527610image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1961
5-th percentile2014
Q12021
median2024
Q32024
95-th percentile2024
Maximum2025
Range64
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.7656761
Coefficient of variation (CV)0.0018625921
Kurtosis13.768701
Mean2021.7396
Median Absolute Deviation (MAD)0
Skewness-2.7285996
Sum76029539
Variance14.180316
MonotonicityNot monotonic
2024-05-20T00:01:56.677757image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
2024 20160
53.6%
2021 3417
 
9.1%
2023 2967
 
7.9%
2020 1885
 
5.0%
2022 1703
 
4.5%
2019 1383
 
3.7%
2018 1185
 
3.2%
2017 936
 
2.5%
2016 783
 
2.1%
2015 659
 
1.8%
Other values (37) 2528
 
6.7%
ValueCountFrequency (%)
1961 1
< 0.1%
1965 1
< 0.1%
1968 1
< 0.1%
1972 1
< 0.1%
1974 1
< 0.1%
1980 1
< 0.1%
1982 1
< 0.1%
1984 2
< 0.1%
1986 1
< 0.1%
1987 1
< 0.1%
ValueCountFrequency (%)
2025 415
 
1.1%
2024 20160
53.6%
2023 2967
 
7.9%
2022 1703
 
4.5%
2021 3417
 
9.1%
2020 1885
 
5.0%
2019 1383
 
3.7%
2018 1185
 
3.2%
2017 936
 
2.5%
2016 783
 
2.1%

mileage
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14766
Distinct (%)39.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22738.545
Minimum0
Maximum962839
Zeros1543
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size293.9 KiB
2024-05-20T00:01:56.841520image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median25.985536
Q336179
95-th percentile101801
Maximum962839
Range962839
Interquartile range (IQR)36173

Descriptive statistics

Standard deviation36902.026
Coefficient of variation (CV)1.6228842
Kurtosis15.311465
Mean22738.545
Median Absolute Deviation (MAD)25.985536
Skewness2.341604
Sum8.5510573 × 108
Variance1.3617595 × 109
MonotonicityNot monotonic
2024-05-20T00:01:56.997923image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 2911
 
7.7%
10 2516
 
6.7%
0 1543
 
4.1%
3 1490
 
4.0%
6 1419
 
3.8%
2 1139
 
3.0%
7 947
 
2.5%
1 920
 
2.4%
11 901
 
2.4%
4 846
 
2.2%
Other values (14756) 22974
61.1%
ValueCountFrequency (%)
0 1543
4.1%
0.78 1
 
< 0.1%
1 920
2.4%
1.09 2
 
< 0.1%
1.44 1
 
< 0.1%
2 1139
3.0%
2.03 1
 
< 0.1%
2.292666667 1
 
< 0.1%
2.51 1
 
< 0.1%
2.644166667 1
 
< 0.1%
ValueCountFrequency (%)
962839 1
< 0.1%
440911 1
< 0.1%
324349 1
< 0.1%
317568 1
< 0.1%
310000 1
< 0.1%
304425 1
< 0.1%
270498 1
< 0.1%
268470 1
< 0.1%
265649 1
< 0.1%
259370 1
< 0.1%

stock_type
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
1.0
21022 
0.0
16584 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters112818
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 21022
55.9%
0.0 16584
44.1%

Length

2024-05-20T00:01:57.143505image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:01:57.282645image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 21022
55.9%
0.0 16584
44.1%

Most occurring characters

ValueCountFrequency (%)
0 54190
48.0%
. 37606
33.3%
1 21022
 
18.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 54190
48.0%
. 37606
33.3%
1 21022
 
18.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 54190
48.0%
. 37606
33.3%
1 21022
 
18.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 54190
48.0%
. 37606
33.3%
1 21022
 
18.6%

model_hashed_0
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
36503 
1.0
 
886
-1.0
 
217

Length

Max length4
Median length3
Mean length3.0057704
Min length3

Characters and Unicode

Total characters113035
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 36503
97.1%
1.0 886
 
2.4%
-1.0 217
 
0.6%

Length

2024-05-20T00:01:57.454911image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:01:57.565002image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 36503
97.1%
1.0 1103
 
2.9%

Most occurring characters

ValueCountFrequency (%)
0 74109
65.6%
. 37606
33.3%
1 1103
 
1.0%
- 217
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 113035
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 74109
65.6%
. 37606
33.3%
1 1103
 
1.0%
- 217
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 113035
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 74109
65.6%
. 37606
33.3%
1 1103
 
1.0%
- 217
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 113035
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 74109
65.6%
. 37606
33.3%
1 1103
 
1.0%
- 217
 
0.2%

model_hashed_1
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
37200 
1.0
 
279
-1.0
 
127

Length

Max length4
Median length3
Mean length3.0033771
Min length3

Characters and Unicode

Total characters112945
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 37200
98.9%
1.0 279
 
0.7%
-1.0 127
 
0.3%

Length

2024-05-20T00:01:57.739227image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:01:57.855489image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 37200
98.9%
1.0 406
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 74806
66.2%
. 37606
33.3%
1 406
 
0.4%
- 127
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112945
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 74806
66.2%
. 37606
33.3%
1 406
 
0.4%
- 127
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112945
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 74806
66.2%
. 37606
33.3%
1 406
 
0.4%
- 127
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112945
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 74806
66.2%
. 37606
33.3%
1 406
 
0.4%
- 127
 
0.1%

model_hashed_2
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
37121 
-1.0
 
306
1.0
 
179

Length

Max length4
Median length3
Mean length3.008137
Min length3

Characters and Unicode

Total characters113124
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 37121
98.7%
-1.0 306
 
0.8%
1.0 179
 
0.5%

Length

2024-05-20T00:01:57.980582image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:01:58.103390image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 37121
98.7%
1.0 485
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 74727
66.1%
. 37606
33.2%
1 485
 
0.4%
- 306
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 113124
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 74727
66.1%
. 37606
33.2%
1 485
 
0.4%
- 306
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 113124
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 74727
66.1%
. 37606
33.2%
1 485
 
0.4%
- 306
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 113124
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 74727
66.1%
. 37606
33.2%
1 485
 
0.4%
- 306
 
0.3%

model_hashed_3
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
36799 
-1.0
 
525
1.0
 
282

Length

Max length4
Median length3
Mean length3.0139605
Min length3

Characters and Unicode

Total characters113343
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 36799
97.9%
-1.0 525
 
1.4%
1.0 282
 
0.7%

Length

2024-05-20T00:01:58.228004image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:01:58.467161image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 36799
97.9%
1.0 807
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 74405
65.6%
. 37606
33.2%
1 807
 
0.7%
- 525
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 113343
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 74405
65.6%
. 37606
33.2%
1 807
 
0.7%
- 525
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 113343
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 74405
65.6%
. 37606
33.2%
1 807
 
0.7%
- 525
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 113343
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 74405
65.6%
. 37606
33.2%
1 807
 
0.7%
- 525
 
0.5%

model_hashed_4
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
37302 
-1.0
 
165
1.0
 
139

Length

Max length4
Median length3
Mean length3.0043876
Min length3

Characters and Unicode

Total characters112983
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 37302
99.2%
-1.0 165
 
0.4%
1.0 139
 
0.4%

Length

2024-05-20T00:01:58.594294image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:01:58.711860image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 37302
99.2%
1.0 304
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 74908
66.3%
. 37606
33.3%
1 304
 
0.3%
- 165
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112983
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 74908
66.3%
. 37606
33.3%
1 304
 
0.3%
- 165
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112983
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 74908
66.3%
. 37606
33.3%
1 304
 
0.3%
- 165
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112983
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 74908
66.3%
. 37606
33.3%
1 304
 
0.3%
- 165
 
0.1%

model_hashed_5
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
37034 
1.0
 
449
-1.0
 
123

Length

Max length4
Median length3
Mean length3.0032708
Min length3

Characters and Unicode

Total characters112941
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 37034
98.5%
1.0 449
 
1.2%
-1.0 123
 
0.3%

Length

2024-05-20T00:01:58.833493image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:01:58.963126image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 37034
98.5%
1.0 572
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0 74640
66.1%
. 37606
33.3%
1 572
 
0.5%
- 123
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112941
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 74640
66.1%
. 37606
33.3%
1 572
 
0.5%
- 123
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112941
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 74640
66.1%
. 37606
33.3%
1 572
 
0.5%
- 123
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112941
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 74640
66.1%
. 37606
33.3%
1 572
 
0.5%
- 123
 
0.1%

model_hashed_6
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
37262 
1.0
 
200
-1.0
 
144

Length

Max length4
Median length3
Mean length3.0038292
Min length3

Characters and Unicode

Total characters112962
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 37262
99.1%
1.0 200
 
0.5%
-1.0 144
 
0.4%

Length

2024-05-20T00:01:59.134276image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:01:59.259931image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 37262
99.1%
1.0 344
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 74868
66.3%
. 37606
33.3%
1 344
 
0.3%
- 144
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112962
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 74868
66.3%
. 37606
33.3%
1 344
 
0.3%
- 144
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112962
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 74868
66.3%
. 37606
33.3%
1 344
 
0.3%
- 144
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112962
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 74868
66.3%
. 37606
33.3%
1 344
 
0.3%
- 144
 
0.1%

model_hashed_7
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
36451 
-1.0
 
665
1.0
 
490

Length

Max length4
Median length3
Mean length3.0176833
Min length3

Characters and Unicode

Total characters113483
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 36451
96.9%
-1.0 665
 
1.8%
1.0 490
 
1.3%

Length

2024-05-20T00:01:59.392040image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:01:59.538620image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 36451
96.9%
1.0 1155
 
3.1%

Most occurring characters

ValueCountFrequency (%)
0 74057
65.3%
. 37606
33.1%
1 1155
 
1.0%
- 665
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 113483
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 74057
65.3%
. 37606
33.1%
1 1155
 
1.0%
- 665
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 113483
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 74057
65.3%
. 37606
33.1%
1 1155
 
1.0%
- 665
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 113483
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 74057
65.3%
. 37606
33.1%
1 1155
 
1.0%
- 665
 
0.6%

model_hashed_8
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
37053 
1.0
 
523
-1.0
 
30

Length

Max length4
Median length3
Mean length3.0007977
Min length3

Characters and Unicode

Total characters112848
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 37053
98.5%
1.0 523
 
1.4%
-1.0 30
 
0.1%

Length

2024-05-20T00:01:59.697610image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:01:59.814145image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 37053
98.5%
1.0 553
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0 74659
66.2%
. 37606
33.3%
1 553
 
0.5%
- 30
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112848
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 74659
66.2%
. 37606
33.3%
1 553
 
0.5%
- 30
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112848
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 74659
66.2%
. 37606
33.3%
1 553
 
0.5%
- 30
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112848
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 74659
66.2%
. 37606
33.3%
1 553
 
0.5%
- 30
 
< 0.1%

model_hashed_9
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
36719 
1.0
 
505
-1.0
 
382

Length

Max length4
Median length3
Mean length3.010158
Min length3

Characters and Unicode

Total characters113200
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 36719
97.6%
1.0 505
 
1.3%
-1.0 382
 
1.0%

Length

2024-05-20T00:01:59.931710image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:00.054977image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 36719
97.6%
1.0 887
 
2.4%

Most occurring characters

ValueCountFrequency (%)
0 74325
65.7%
. 37606
33.2%
1 887
 
0.8%
- 382
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 113200
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 74325
65.7%
. 37606
33.2%
1 887
 
0.8%
- 382
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 113200
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 74325
65.7%
. 37606
33.2%
1 887
 
0.8%
- 382
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 113200
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 74325
65.7%
. 37606
33.2%
1 887
 
0.8%
- 382
 
0.3%

model_hashed_10
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
37056 
1.0
 
281
-1.0
 
269

Length

Max length4
Median length3
Mean length3.0071531
Min length3

Characters and Unicode

Total characters113087
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 37056
98.5%
1.0 281
 
0.7%
-1.0 269
 
0.7%

Length

2024-05-20T00:02:00.181098image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:00.299642image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 37056
98.5%
1.0 550
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0 74662
66.0%
. 37606
33.3%
1 550
 
0.5%
- 269
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 113087
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 74662
66.0%
. 37606
33.3%
1 550
 
0.5%
- 269
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 113087
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 74662
66.0%
. 37606
33.3%
1 550
 
0.5%
- 269
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 113087
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 74662
66.0%
. 37606
33.3%
1 550
 
0.5%
- 269
 
0.2%

model_hashed_11
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
37321 
1.0
 
186
-1.0
 
99

Length

Max length4
Median length3
Mean length3.0026326
Min length3

Characters and Unicode

Total characters112917
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 37321
99.2%
1.0 186
 
0.5%
-1.0 99
 
0.3%

Length

2024-05-20T00:02:00.424178image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:00.541732image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 37321
99.2%
1.0 285
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 74927
66.4%
. 37606
33.3%
1 285
 
0.3%
- 99
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112917
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 74927
66.4%
. 37606
33.3%
1 285
 
0.3%
- 99
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112917
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 74927
66.4%
. 37606
33.3%
1 285
 
0.3%
- 99
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112917
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 74927
66.4%
. 37606
33.3%
1 285
 
0.3%
- 99
 
0.1%

model_hashed_12
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
36112 
-1.0
 
1155
1.0
 
339

Length

Max length4
Median length3
Mean length3.0307132
Min length3

Characters and Unicode

Total characters113973
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 36112
96.0%
-1.0 1155
 
3.1%
1.0 339
 
0.9%

Length

2024-05-20T00:02:00.664365image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:00.793517image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 36112
96.0%
1.0 1494
 
4.0%

Most occurring characters

ValueCountFrequency (%)
0 73718
64.7%
. 37606
33.0%
1 1494
 
1.3%
- 1155
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 113973
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 73718
64.7%
. 37606
33.0%
1 1494
 
1.3%
- 1155
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 113973
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 73718
64.7%
. 37606
33.0%
1 1494
 
1.3%
- 1155
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 113973
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 73718
64.7%
. 37606
33.0%
1 1494
 
1.3%
- 1155
 
1.0%

model_hashed_13
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
36053 
1.0
 
901
-1.0
 
652

Length

Max length4
Median length3
Mean length3.0173377
Min length3

Characters and Unicode

Total characters113470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 36053
95.9%
1.0 901
 
2.4%
-1.0 652
 
1.7%

Length

2024-05-20T00:02:00.934077image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:01.053706image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 36053
95.9%
1.0 1553
 
4.1%

Most occurring characters

ValueCountFrequency (%)
0 73659
64.9%
. 37606
33.1%
1 1553
 
1.4%
- 652
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 113470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 73659
64.9%
. 37606
33.1%
1 1553
 
1.4%
- 652
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 113470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 73659
64.9%
. 37606
33.1%
1 1553
 
1.4%
- 652
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 113470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 73659
64.9%
. 37606
33.1%
1 1553
 
1.4%
- 652
 
0.6%

model_hashed_14
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
36578 
1.0
 
614
-1.0
 
414

Length

Max length4
Median length3
Mean length3.0110089
Min length3

Characters and Unicode

Total characters113232
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 36578
97.3%
1.0 614
 
1.6%
-1.0 414
 
1.1%

Length

2024-05-20T00:02:01.192671image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:01.308068image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 36578
97.3%
1.0 1028
 
2.7%

Most occurring characters

ValueCountFrequency (%)
0 74184
65.5%
. 37606
33.2%
1 1028
 
0.9%
- 414
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 113232
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 74184
65.5%
. 37606
33.2%
1 1028
 
0.9%
- 414
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 113232
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 74184
65.5%
. 37606
33.2%
1 1028
 
0.9%
- 414
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 113232
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 74184
65.5%
. 37606
33.2%
1 1028
 
0.9%
- 414
 
0.4%

model_hashed_15
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
37332 
-1.0
 
176
1.0
 
98

Length

Max length4
Median length3
Mean length3.0046801
Min length3

Characters and Unicode

Total characters112994
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 37332
99.3%
-1.0 176
 
0.5%
1.0 98
 
0.3%

Length

2024-05-20T00:02:01.429625image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:01.551946image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 37332
99.3%
1.0 274
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 74938
66.3%
. 37606
33.3%
1 274
 
0.2%
- 176
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112994
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 74938
66.3%
. 37606
33.3%
1 274
 
0.2%
- 176
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112994
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 74938
66.3%
. 37606
33.3%
1 274
 
0.2%
- 176
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112994
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 74938
66.3%
. 37606
33.3%
1 274
 
0.2%
- 176
 
0.2%

model_hashed_16
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
36921 
-1.0
 
625
1.0
 
60

Length

Max length4
Median length3
Mean length3.0166197
Min length3

Characters and Unicode

Total characters113443
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 36921
98.2%
-1.0 625
 
1.7%
1.0 60
 
0.2%

Length

2024-05-20T00:02:01.711141image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:01.894749image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 36921
98.2%
1.0 685
 
1.8%

Most occurring characters

ValueCountFrequency (%)
0 74527
65.7%
. 37606
33.1%
1 685
 
0.6%
- 625
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 113443
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 74527
65.7%
. 37606
33.1%
1 685
 
0.6%
- 625
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 113443
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 74527
65.7%
. 37606
33.1%
1 685
 
0.6%
- 625
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 113443
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 74527
65.7%
. 37606
33.1%
1 685
 
0.6%
- 625
 
0.6%

model_hashed_17
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
36827 
-1.0
 
447
1.0
 
332

Length

Max length4
Median length3
Mean length3.0118864
Min length3

Characters and Unicode

Total characters113265
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 36827
97.9%
-1.0 447
 
1.2%
1.0 332
 
0.9%

Length

2024-05-20T00:02:02.026742image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:02.137288image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 36827
97.9%
1.0 779
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 74433
65.7%
. 37606
33.2%
1 779
 
0.7%
- 447
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 113265
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 74433
65.7%
. 37606
33.2%
1 779
 
0.7%
- 447
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 113265
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 74433
65.7%
. 37606
33.2%
1 779
 
0.7%
- 447
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 113265
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 74433
65.7%
. 37606
33.2%
1 779
 
0.7%
- 447
 
0.4%

model_hashed_18
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
36875 
1.0
 
664
-1.0
 
67

Length

Max length4
Median length3
Mean length3.0017816
Min length3

Characters and Unicode

Total characters112885
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 36875
98.1%
1.0 664
 
1.8%
-1.0 67
 
0.2%

Length

2024-05-20T00:02:02.261944image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:02.375977image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 36875
98.1%
1.0 731
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 74481
66.0%
. 37606
33.3%
1 731
 
0.6%
- 67
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112885
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 74481
66.0%
. 37606
33.3%
1 731
 
0.6%
- 67
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112885
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 74481
66.0%
. 37606
33.3%
1 731
 
0.6%
- 67
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112885
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 74481
66.0%
. 37606
33.3%
1 731
 
0.6%
- 67
 
0.1%

model_hashed_19
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
36693 
1.0
 
667
-1.0
 
246

Length

Max length4
Median length3
Mean length3.0065415
Min length3

Characters and Unicode

Total characters113064
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 36693
97.6%
1.0 667
 
1.8%
-1.0 246
 
0.7%

Length

2024-05-20T00:02:02.499500image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:02.617044image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 36693
97.6%
1.0 913
 
2.4%

Most occurring characters

ValueCountFrequency (%)
0 74299
65.7%
. 37606
33.3%
1 913
 
0.8%
- 246
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 113064
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 74299
65.7%
. 37606
33.3%
1 913
 
0.8%
- 246
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 113064
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 74299
65.7%
. 37606
33.3%
1 913
 
0.8%
- 246
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 113064
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 74299
65.7%
. 37606
33.3%
1 913
 
0.8%
- 246
 
0.2%

model_hashed_20
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
36118 
1.0
 
925
-1.0
 
563

Length

Max length4
Median length3
Mean length3.014971
Min length3

Characters and Unicode

Total characters113381
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 36118
96.0%
1.0 925
 
2.5%
-1.0 563
 
1.5%

Length

2024-05-20T00:02:02.797797image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:03.048997image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 36118
96.0%
1.0 1488
 
4.0%

Most occurring characters

ValueCountFrequency (%)
0 73724
65.0%
. 37606
33.2%
1 1488
 
1.3%
- 563
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 113381
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 73724
65.0%
. 37606
33.2%
1 1488
 
1.3%
- 563
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 113381
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 73724
65.0%
. 37606
33.2%
1 1488
 
1.3%
- 563
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 113381
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 73724
65.0%
. 37606
33.2%
1 1488
 
1.3%
- 563
 
0.5%

model_hashed_21
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
37118 
1.0
 
402
-1.0
 
86

Length

Max length4
Median length3
Mean length3.0022869
Min length3

Characters and Unicode

Total characters112904
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 37118
98.7%
1.0 402
 
1.1%
-1.0 86
 
0.2%

Length

2024-05-20T00:02:03.209692image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:03.408222image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 37118
98.7%
1.0 488
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 74724
66.2%
. 37606
33.3%
1 488
 
0.4%
- 86
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112904
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 74724
66.2%
. 37606
33.3%
1 488
 
0.4%
- 86
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112904
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 74724
66.2%
. 37606
33.3%
1 488
 
0.4%
- 86
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112904
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 74724
66.2%
. 37606
33.3%
1 488
 
0.4%
- 86
 
0.1%

model_hashed_22
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
36642 
-1.0
 
770
1.0
 
194

Length

Max length4
Median length3
Mean length3.0204755
Min length3

Characters and Unicode

Total characters113588
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 36642
97.4%
-1.0 770
 
2.0%
1.0 194
 
0.5%

Length

2024-05-20T00:02:03.537833image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:03.671451image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 36642
97.4%
1.0 964
 
2.6%

Most occurring characters

ValueCountFrequency (%)
0 74248
65.4%
. 37606
33.1%
1 964
 
0.8%
- 770
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 113588
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 74248
65.4%
. 37606
33.1%
1 964
 
0.8%
- 770
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 113588
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 74248
65.4%
. 37606
33.1%
1 964
 
0.8%
- 770
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 113588
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 74248
65.4%
. 37606
33.1%
1 964
 
0.8%
- 770
 
0.7%

model_hashed_23
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
36672 
1.0
 
510
-1.0
 
424

Length

Max length4
Median length3
Mean length3.0112748
Min length3

Characters and Unicode

Total characters113242
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 36672
97.5%
1.0 510
 
1.4%
-1.0 424
 
1.1%

Length

2024-05-20T00:02:03.793522image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:03.912077image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 36672
97.5%
1.0 934
 
2.5%

Most occurring characters

ValueCountFrequency (%)
0 74278
65.6%
. 37606
33.2%
1 934
 
0.8%
- 424
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 113242
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 74278
65.6%
. 37606
33.2%
1 934
 
0.8%
- 424
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 113242
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 74278
65.6%
. 37606
33.2%
1 934
 
0.8%
- 424
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 113242
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 74278
65.6%
. 37606
33.2%
1 934
 
0.8%
- 424
 
0.4%

model_hashed_24
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
37034 
1.0
 
354
-1.0
 
218

Length

Max length4
Median length3
Mean length3.0057969
Min length3

Characters and Unicode

Total characters113036
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 37034
98.5%
1.0 354
 
0.9%
-1.0 218
 
0.6%

Length

2024-05-20T00:02:04.091263image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:04.238870image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 37034
98.5%
1.0 572
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0 74640
66.0%
. 37606
33.3%
1 572
 
0.5%
- 218
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 113036
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 74640
66.0%
. 37606
33.3%
1 572
 
0.5%
- 218
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 113036
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 74640
66.0%
. 37606
33.3%
1 572
 
0.5%
- 218
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 113036
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 74640
66.0%
. 37606
33.3%
1 572
 
0.5%
- 218
 
0.2%

model_hashed_25
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
37085 
-1.0
 
378
1.0
 
143

Length

Max length4
Median length3
Mean length3.0100516
Min length3

Characters and Unicode

Total characters113196
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 37085
98.6%
-1.0 378
 
1.0%
1.0 143
 
0.4%

Length

2024-05-20T00:02:04.364385image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:04.537313image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 37085
98.6%
1.0 521
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 74691
66.0%
. 37606
33.2%
1 521
 
0.5%
- 378
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 113196
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 74691
66.0%
. 37606
33.2%
1 521
 
0.5%
- 378
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 113196
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 74691
66.0%
. 37606
33.2%
1 521
 
0.5%
- 378
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 113196
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 74691
66.0%
. 37606
33.2%
1 521
 
0.5%
- 378
 
0.3%

model_hashed_26
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
37214 
1.0
 
352
-1.0
 
40

Length

Max length4
Median length3
Mean length3.0010637
Min length3

Characters and Unicode

Total characters112858
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 37214
99.0%
1.0 352
 
0.9%
-1.0 40
 
0.1%

Length

2024-05-20T00:02:04.723723image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:04.838237image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 37214
99.0%
1.0 392
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0 74820
66.3%
. 37606
33.3%
1 392
 
0.3%
- 40
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112858
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 74820
66.3%
. 37606
33.3%
1 392
 
0.3%
- 40
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112858
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 74820
66.3%
. 37606
33.3%
1 392
 
0.3%
- 40
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112858
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 74820
66.3%
. 37606
33.3%
1 392
 
0.3%
- 40
 
< 0.1%

model_hashed_27
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
36072 
1.0
 
1151
-1.0
 
383

Length

Max length4
Median length3
Mean length3.0101845
Min length3

Characters and Unicode

Total characters113201
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 36072
95.9%
1.0 1151
 
3.1%
-1.0 383
 
1.0%

Length

2024-05-20T00:02:04.964685image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:05.080419image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 36072
95.9%
1.0 1534
 
4.1%

Most occurring characters

ValueCountFrequency (%)
0 73678
65.1%
. 37606
33.2%
1 1534
 
1.4%
- 383
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 113201
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 73678
65.1%
. 37606
33.2%
1 1534
 
1.4%
- 383
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 113201
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 73678
65.1%
. 37606
33.2%
1 1534
 
1.4%
- 383
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 113201
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 73678
65.1%
. 37606
33.2%
1 1534
 
1.4%
- 383
 
0.3%

model_hashed_28
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
36730 
-1.0
 
513
1.0
 
363

Length

Max length4
Median length3
Mean length3.0136414
Min length3

Characters and Unicode

Total characters113331
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 36730
97.7%
-1.0 513
 
1.4%
1.0 363
 
1.0%

Length

2024-05-20T00:02:05.210976image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:05.328117image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 36730
97.7%
1.0 876
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 74336
65.6%
. 37606
33.2%
1 876
 
0.8%
- 513
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 113331
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 74336
65.6%
. 37606
33.2%
1 876
 
0.8%
- 513
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 113331
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 74336
65.6%
. 37606
33.2%
1 876
 
0.8%
- 513
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 113331
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 74336
65.6%
. 37606
33.2%
1 876
 
0.8%
- 513
 
0.5%

model_hashed_29
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
36162 
1.0
 
1179
-1.0
 
265

Length

Max length4
Median length3
Mean length3.0070467
Min length3

Characters and Unicode

Total characters113083
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 36162
96.2%
1.0 1179
 
3.1%
-1.0 265
 
0.7%

Length

2024-05-20T00:02:05.459712image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:05.578277image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 36162
96.2%
1.0 1444
 
3.8%

Most occurring characters

ValueCountFrequency (%)
0 73768
65.2%
. 37606
33.3%
1 1444
 
1.3%
- 265
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 113083
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 73768
65.2%
. 37606
33.3%
1 1444
 
1.3%
- 265
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 113083
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 73768
65.2%
. 37606
33.3%
1 1444
 
1.3%
- 265
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 113083
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 73768
65.2%
. 37606
33.3%
1 1444
 
1.3%
- 265
 
0.2%

model_hashed_30
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
36654 
1.0
 
495
-1.0
 
457

Length

Max length4
Median length3
Mean length3.0121523
Min length3

Characters and Unicode

Total characters113275
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 36654
97.5%
1.0 495
 
1.3%
-1.0 457
 
1.2%

Length

2024-05-20T00:02:05.725383image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:06.013375image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 36654
97.5%
1.0 952
 
2.5%

Most occurring characters

ValueCountFrequency (%)
0 74260
65.6%
. 37606
33.2%
1 952
 
0.8%
- 457
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 113275
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 74260
65.6%
. 37606
33.2%
1 952
 
0.8%
- 457
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 113275
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 74260
65.6%
. 37606
33.2%
1 952
 
0.8%
- 457
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 113275
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 74260
65.6%
. 37606
33.2%
1 952
 
0.8%
- 457
 
0.4%

model_hashed_31
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
37264 
1.0
 
257
-1.0
 
85

Length

Max length4
Median length3
Mean length3.0022603
Min length3

Characters and Unicode

Total characters112903
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 37264
99.1%
1.0 257
 
0.7%
-1.0 85
 
0.2%

Length

2024-05-20T00:02:06.253615image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:06.415572image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 37264
99.1%
1.0 342
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 74870
66.3%
. 37606
33.3%
1 342
 
0.3%
- 85
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112903
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 74870
66.3%
. 37606
33.3%
1 342
 
0.3%
- 85
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112903
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 74870
66.3%
. 37606
33.3%
1 342
 
0.3%
- 85
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112903
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 74870
66.3%
. 37606
33.3%
1 342
 
0.3%
- 85
 
0.1%

model_hashed_32
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
36851 
1.0
 
611
-1.0
 
144

Length

Max length4
Median length3
Mean length3.0038292
Min length3

Characters and Unicode

Total characters112962
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 36851
98.0%
1.0 611
 
1.6%
-1.0 144
 
0.4%

Length

2024-05-20T00:02:06.620244image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:06.732797image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 36851
98.0%
1.0 755
 
2.0%

Most occurring characters

ValueCountFrequency (%)
0 74457
65.9%
. 37606
33.3%
1 755
 
0.7%
- 144
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112962
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 74457
65.9%
. 37606
33.3%
1 755
 
0.7%
- 144
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112962
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 74457
65.9%
. 37606
33.3%
1 755
 
0.7%
- 144
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112962
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 74457
65.9%
. 37606
33.3%
1 755
 
0.7%
- 144
 
0.1%

model_hashed_33
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
36829 
-1.0
 
754
1.0
 
23

Length

Max length4
Median length3
Mean length3.02005
Min length3

Characters and Unicode

Total characters113572
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row-1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 36829
97.9%
-1.0 754
 
2.0%
1.0 23
 
0.1%

Length

2024-05-20T00:02:06.862355image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:06.979460image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 36829
97.9%
1.0 777
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 74435
65.5%
. 37606
33.1%
1 777
 
0.7%
- 754
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 113572
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 74435
65.5%
. 37606
33.1%
1 777
 
0.7%
- 754
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 113572
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 74435
65.5%
. 37606
33.1%
1 777
 
0.7%
- 754
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 113572
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 74435
65.5%
. 37606
33.1%
1 777
 
0.7%
- 754
 
0.7%

model_hashed_34
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
36996 
-1.0
 
331
1.0
 
279

Length

Max length4
Median length3
Mean length3.0088018
Min length3

Characters and Unicode

Total characters113149
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 36996
98.4%
-1.0 331
 
0.9%
1.0 279
 
0.7%

Length

2024-05-20T00:02:07.106056image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:07.221593image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 36996
98.4%
1.0 610
 
1.6%

Most occurring characters

ValueCountFrequency (%)
0 74602
65.9%
. 37606
33.2%
1 610
 
0.5%
- 331
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 113149
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 74602
65.9%
. 37606
33.2%
1 610
 
0.5%
- 331
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 113149
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 74602
65.9%
. 37606
33.2%
1 610
 
0.5%
- 331
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 113149
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 74602
65.9%
. 37606
33.2%
1 610
 
0.5%
- 331
 
0.3%

model_hashed_35
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
37290 
-1.0
 
167
1.0
 
149

Length

Max length4
Median length3
Mean length3.0044408
Min length3

Characters and Unicode

Total characters112985
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 37290
99.2%
-1.0 167
 
0.4%
1.0 149
 
0.4%

Length

2024-05-20T00:02:07.347012image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:07.465543image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 37290
99.2%
1.0 316
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 74896
66.3%
. 37606
33.3%
1 316
 
0.3%
- 167
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112985
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 74896
66.3%
. 37606
33.3%
1 316
 
0.3%
- 167
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112985
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 74896
66.3%
. 37606
33.3%
1 316
 
0.3%
- 167
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112985
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 74896
66.3%
. 37606
33.3%
1 316
 
0.3%
- 167
 
0.1%

model_hashed_36
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
37428 
1.0
 
133
-1.0
 
45

Length

Max length4
Median length3
Mean length3.0011966
Min length3

Characters and Unicode

Total characters112863
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 37428
99.5%
1.0 133
 
0.4%
-1.0 45
 
0.1%

Length

2024-05-20T00:02:07.586607image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:07.837729image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 37428
99.5%
1.0 178
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 75034
66.5%
. 37606
33.3%
1 178
 
0.2%
- 45
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112863
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 75034
66.5%
. 37606
33.3%
1 178
 
0.2%
- 45
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112863
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 75034
66.5%
. 37606
33.3%
1 178
 
0.2%
- 45
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112863
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 75034
66.5%
. 37606
33.3%
1 178
 
0.2%
- 45
 
< 0.1%

model_hashed_37
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
36559 
1.0
 
643
-1.0
 
404

Length

Max length4
Median length3
Mean length3.010743
Min length3

Characters and Unicode

Total characters113222
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 36559
97.2%
1.0 643
 
1.7%
-1.0 404
 
1.1%

Length

2024-05-20T00:02:07.968296image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:08.080532image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 36559
97.2%
1.0 1047
 
2.8%

Most occurring characters

ValueCountFrequency (%)
0 74165
65.5%
. 37606
33.2%
1 1047
 
0.9%
- 404
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 113222
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 74165
65.5%
. 37606
33.2%
1 1047
 
0.9%
- 404
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 113222
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 74165
65.5%
. 37606
33.2%
1 1047
 
0.9%
- 404
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 113222
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 74165
65.5%
. 37606
33.2%
1 1047
 
0.9%
- 404
 
0.4%

model_hashed_38
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
37064 
-1.0
 
338
1.0
 
204

Length

Max length4
Median length3
Mean length3.0089879
Min length3

Characters and Unicode

Total characters113156
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 37064
98.6%
-1.0 338
 
0.9%
1.0 204
 
0.5%

Length

2024-05-20T00:02:08.207108image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:08.316648image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 37064
98.6%
1.0 542
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 74670
66.0%
. 37606
33.2%
1 542
 
0.5%
- 338
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 113156
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 74670
66.0%
. 37606
33.2%
1 542
 
0.5%
- 338
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 113156
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 74670
66.0%
. 37606
33.2%
1 542
 
0.5%
- 338
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 113156
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 74670
66.0%
. 37606
33.2%
1 542
 
0.5%
- 338
 
0.3%

model_hashed_39
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
37040 
1.0
 
403
-1.0
 
163

Length

Max length4
Median length3
Mean length3.0043344
Min length3

Characters and Unicode

Total characters112981
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 37040
98.5%
1.0 403
 
1.1%
-1.0 163
 
0.4%

Length

2024-05-20T00:02:08.473624image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:08.615960image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 37040
98.5%
1.0 566
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0 74646
66.1%
. 37606
33.3%
1 566
 
0.5%
- 163
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112981
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 74646
66.1%
. 37606
33.3%
1 566
 
0.5%
- 163
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112981
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 74646
66.1%
. 37606
33.3%
1 566
 
0.5%
- 163
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112981
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 74646
66.1%
. 37606
33.3%
1 566
 
0.5%
- 163
 
0.1%

model_hashed_40
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
36623 
-1.0
 
715
1.0
 
268

Length

Max length4
Median length3
Mean length3.0190129
Min length3

Characters and Unicode

Total characters113533
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 36623
97.4%
-1.0 715
 
1.9%
1.0 268
 
0.7%

Length

2024-05-20T00:02:08.735563image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:08.870134image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 36623
97.4%
1.0 983
 
2.6%

Most occurring characters

ValueCountFrequency (%)
0 74229
65.4%
. 37606
33.1%
1 983
 
0.9%
- 715
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 113533
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 74229
65.4%
. 37606
33.1%
1 983
 
0.9%
- 715
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 113533
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 74229
65.4%
. 37606
33.1%
1 983
 
0.9%
- 715
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 113533
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 74229
65.4%
. 37606
33.1%
1 983
 
0.9%
- 715
 
0.6%

model_hashed_41
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
37403 
1.0
 
203

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters112818
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 37403
99.5%
1.0 203
 
0.5%

Length

2024-05-20T00:02:08.995178image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:09.106785image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 37403
99.5%
1.0 203
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 75009
66.5%
. 37606
33.3%
1 203
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 75009
66.5%
. 37606
33.3%
1 203
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 75009
66.5%
. 37606
33.3%
1 203
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 75009
66.5%
. 37606
33.3%
1 203
 
0.2%

model_hashed_42
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
37298 
1.0
 
167
-1.0
 
141

Length

Max length4
Median length3
Mean length3.0037494
Min length3

Characters and Unicode

Total characters112959
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 37298
99.2%
1.0 167
 
0.4%
-1.0 141
 
0.4%

Length

2024-05-20T00:02:09.229301image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:09.348897image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 37298
99.2%
1.0 308
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 74904
66.3%
. 37606
33.3%
1 308
 
0.3%
- 141
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112959
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 74904
66.3%
. 37606
33.3%
1 308
 
0.3%
- 141
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112959
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 74904
66.3%
. 37606
33.3%
1 308
 
0.3%
- 141
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112959
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 74904
66.3%
. 37606
33.3%
1 308
 
0.3%
- 141
 
0.1%

model_hashed_43
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
37089 
-1.0
 
486
1.0
 
31

Length

Max length4
Median length3
Mean length3.0129235
Min length3

Characters and Unicode

Total characters113304
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 37089
98.6%
-1.0 486
 
1.3%
1.0 31
 
0.1%

Length

2024-05-20T00:02:09.473365image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:09.594412image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 37089
98.6%
1.0 517
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 74695
65.9%
. 37606
33.2%
1 517
 
0.5%
- 486
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 113304
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 74695
65.9%
. 37606
33.2%
1 517
 
0.5%
- 486
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 113304
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 74695
65.9%
. 37606
33.2%
1 517
 
0.5%
- 486
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 113304
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 74695
65.9%
. 37606
33.2%
1 517
 
0.5%
- 486
 
0.4%

model_hashed_44
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
37432 
1.0
 
124
-1.0
 
50

Length

Max length4
Median length3
Mean length3.0013296
Min length3

Characters and Unicode

Total characters112868
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 37432
99.5%
1.0 124
 
0.3%
-1.0 50
 
0.1%

Length

2024-05-20T00:02:09.718956image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:09.831515image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 37432
99.5%
1.0 174
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 75038
66.5%
. 37606
33.3%
1 174
 
0.2%
- 50
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112868
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 75038
66.5%
. 37606
33.3%
1 174
 
0.2%
- 50
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112868
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 75038
66.5%
. 37606
33.3%
1 174
 
0.2%
- 50
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112868
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 75038
66.5%
. 37606
33.3%
1 174
 
0.2%
- 50
 
< 0.1%

model_hashed_45
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
37053 
-1.0
 
342
1.0
 
211

Length

Max length4
Median length3
Mean length3.0090943
Min length3

Characters and Unicode

Total characters113160
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 37053
98.5%
-1.0 342
 
0.9%
1.0 211
 
0.6%

Length

2024-05-20T00:02:09.964053image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:10.078845image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 37053
98.5%
1.0 553
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0 74659
66.0%
. 37606
33.2%
1 553
 
0.5%
- 342
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 113160
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 74659
66.0%
. 37606
33.2%
1 553
 
0.5%
- 342
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 113160
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 74659
66.0%
. 37606
33.2%
1 553
 
0.5%
- 342
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 113160
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 74659
66.0%
. 37606
33.2%
1 553
 
0.5%
- 342
 
0.3%

model_hashed_46
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
37042 
-1.0
 
301
1.0
 
263

Length

Max length4
Median length3
Mean length3.008004
Min length3

Characters and Unicode

Total characters113119
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 37042
98.5%
-1.0 301
 
0.8%
1.0 263
 
0.7%

Length

2024-05-20T00:02:10.202919image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:10.327521image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 37042
98.5%
1.0 564
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0 74648
66.0%
. 37606
33.2%
1 564
 
0.5%
- 301
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 113119
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 74648
66.0%
. 37606
33.2%
1 564
 
0.5%
- 301
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 113119
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 74648
66.0%
. 37606
33.2%
1 564
 
0.5%
- 301
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 113119
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 74648
66.0%
. 37606
33.2%
1 564
 
0.5%
- 301
 
0.3%

model_hashed_47
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
37269 
-1.0
 
293
1.0
 
44

Length

Max length4
Median length3
Mean length3.0077913
Min length3

Characters and Unicode

Total characters113111
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 37269
99.1%
-1.0 293
 
0.8%
1.0 44
 
0.1%

Length

2024-05-20T00:02:10.468178image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:10.584783image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 37269
99.1%
1.0 337
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 74875
66.2%
. 37606
33.2%
1 337
 
0.3%
- 293
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 113111
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 74875
66.2%
. 37606
33.2%
1 337
 
0.3%
- 293
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 113111
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 74875
66.2%
. 37606
33.2%
1 337
 
0.3%
- 293
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 113111
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 74875
66.2%
. 37606
33.2%
1 337
 
0.3%
- 293
 
0.3%

model_hashed_48
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
35731 
-1.0
 
1212
1.0
 
663

Length

Max length4
Median length3
Mean length3.0322289
Min length3

Characters and Unicode

Total characters114030
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row-1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 35731
95.0%
-1.0 1212
 
3.2%
1.0 663
 
1.8%

Length

2024-05-20T00:02:10.796407image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:10.916600image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 35731
95.0%
1.0 1875
 
5.0%

Most occurring characters

ValueCountFrequency (%)
0 73337
64.3%
. 37606
33.0%
1 1875
 
1.6%
- 1212
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 114030
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 73337
64.3%
. 37606
33.0%
1 1875
 
1.6%
- 1212
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 114030
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 73337
64.3%
. 37606
33.0%
1 1875
 
1.6%
- 1212
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 114030
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 73337
64.3%
. 37606
33.0%
1 1875
 
1.6%
- 1212
 
1.1%

model_hashed_49
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
37255 
-1.0
 
240
1.0
 
111

Length

Max length4
Median length3
Mean length3.006382
Min length3

Characters and Unicode

Total characters113058
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 37255
99.1%
-1.0 240
 
0.6%
1.0 111
 
0.3%

Length

2024-05-20T00:02:11.082760image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:11.279464image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 37255
99.1%
1.0 351
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 74861
66.2%
. 37606
33.3%
1 351
 
0.3%
- 240
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 113058
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 74861
66.2%
. 37606
33.3%
1 351
 
0.3%
- 240
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 113058
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 74861
66.2%
. 37606
33.3%
1 351
 
0.3%
- 240
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 113058
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 74861
66.2%
. 37606
33.3%
1 351
 
0.3%
- 240
 
0.2%

model_hashed_50
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
37063 
-1.0
 
395
1.0
 
148

Length

Max length4
Median length3
Mean length3.0105036
Min length3

Characters and Unicode

Total characters113213
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 37063
98.6%
-1.0 395
 
1.1%
1.0 148
 
0.4%

Length

2024-05-20T00:02:11.588871image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:11.902299image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 37063
98.6%
1.0 543
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 74669
66.0%
. 37606
33.2%
1 543
 
0.5%
- 395
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 113213
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 74669
66.0%
. 37606
33.2%
1 543
 
0.5%
- 395
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 113213
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 74669
66.0%
. 37606
33.2%
1 543
 
0.5%
- 395
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 113213
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 74669
66.0%
. 37606
33.2%
1 543
 
0.5%
- 395
 
0.3%

model_hashed_51
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
36614 
-1.0
 
501
1.0
 
491

Length

Max length4
Median length3
Mean length3.0133223
Min length3

Characters and Unicode

Total characters113319
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 36614
97.4%
-1.0 501
 
1.3%
1.0 491
 
1.3%

Length

2024-05-20T00:02:12.174499image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:12.309930image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 36614
97.4%
1.0 992
 
2.6%

Most occurring characters

ValueCountFrequency (%)
0 74220
65.5%
. 37606
33.2%
1 992
 
0.9%
- 501
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 113319
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 74220
65.5%
. 37606
33.2%
1 992
 
0.9%
- 501
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 113319
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 74220
65.5%
. 37606
33.2%
1 992
 
0.9%
- 501
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 113319
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 74220
65.5%
. 37606
33.2%
1 992
 
0.9%
- 501
 
0.4%

exterior_color_x0
Real number (ℝ)

HIGH CORRELATION 

Distinct1898
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.24655131
Minimum-3.0589778
Maximum1.7655432
Zeros30
Zeros (%)0.1%
Negative25277
Negative (%)67.2%
Memory size293.9 KiB
2024-05-20T00:02:12.629329image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-3.0589778
5-th percentile-1.6798811
Q1-0.84383506
median-0.43715855
Q30.2650055
95-th percentile1.2626994
Maximum1.7655432
Range4.8245211
Interquartile range (IQR)1.1088406

Descriptive statistics

Standard deviation0.9208845
Coefficient of variation (CV)-3.7350624
Kurtosis0.04737661
Mean-0.24655131
Median Absolute Deviation (MAD)0.48601467
Skewness0.12766741
Sum-9271.8084
Variance0.84802827
MonotonicityNot monotonic
2024-05-20T00:02:12.794462image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.7511814237 2282
 
6.1%
1.60383141 952
 
2.5%
1.256851673 929
 
2.5%
-0.5318110585 911
 
2.4%
0.451739043 681
 
1.8%
-0.03456513211 488
 
1.3%
-0.9413885474 434
 
1.2%
-0.993098259 429
 
1.1%
-0.2056239396 428
 
1.1%
-3.058977842 409
 
1.1%
Other values (1888) 29663
78.9%
ValueCountFrequency (%)
-3.058977842 409
1.1%
-2.341434002 153
 
0.4%
-2.117038488 1
 
< 0.1%
-1.961747289 2
 
< 0.1%
-1.952655673 1
 
< 0.1%
-1.905079603 10
 
< 0.1%
-1.878854156 5
 
< 0.1%
-1.868785262 1
 
< 0.1%
-1.859131932 1
 
< 0.1%
-1.840353489 1
 
< 0.1%
ValueCountFrequency (%)
1.765543222 48
 
0.1%
1.761154652 8
 
< 0.1%
1.717839837 78
 
0.2%
1.701323867 1
 
< 0.1%
1.606879234 137
 
0.4%
1.60383141 952
2.5%
1.572880983 5
 
< 0.1%
1.545447469 4
 
< 0.1%
1.539661765 2
 
< 0.1%
1.537572265 16
 
< 0.1%

exterior_color_x1
Real number (ℝ)

Distinct1898
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.70135798
Minimum-1.0462689
Maximum2.3650715
Zeros30
Zeros (%)0.1%
Negative3698
Negative (%)9.8%
Memory size293.9 KiB
2024-05-20T00:02:13.005778image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-1.0462689
5-th percentile-0.081496693
Q10.17771569
median0.64382613
Q31.1491094
95-th percentile1.7051448
Maximum2.3650715
Range3.4113405
Interquartile range (IQR)0.97139367

Descriptive statistics

Standard deviation0.59193777
Coefficient of variation (CV)0.84398808
Kurtosis-0.75849394
Mean0.70135798
Median Absolute Deviation (MAD)0.4695313
Skewness0.40349357
Sum26375.268
Variance0.35039032
MonotonicityNot monotonic
2024-05-20T00:02:13.171595image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.597052574 2282
 
6.1%
0.6729584932 952
 
2.5%
0.7106455564 929
 
2.5%
-0.02926694602 911
 
2.4%
1.337975144 681
 
1.8%
0.6428204775 488
 
1.3%
0.05307491124 434
 
1.2%
0.8483321071 429
 
1.1%
1.591193676 428
 
1.1%
0.1777156889 409
 
1.1%
Other values (1888) 29663
78.9%
ValueCountFrequency (%)
-1.04626894 40
0.1%
-0.8405029774 11
 
< 0.1%
-0.6971178055 3
 
< 0.1%
-0.6439523101 1
 
< 0.1%
-0.4955306649 2
 
< 0.1%
-0.4575667381 4
 
< 0.1%
-0.4569045603 4
 
< 0.1%
-0.4348849654 56
0.1%
-0.4342766404 3
 
< 0.1%
-0.4260134697 1
 
< 0.1%
ValueCountFrequency (%)
2.365071535 3
 
< 0.1%
2.342634439 1
 
< 0.1%
2.211446524 5
 
< 0.1%
2.118834496 351
0.9%
2.082643747 247
0.7%
1.969843507 1
 
< 0.1%
1.922412515 10
 
< 0.1%
1.913381219 63
 
0.2%
1.90028429 1
 
< 0.1%
1.898656607 105
 
0.3%

exterior_color_x2
Real number (ℝ)

HIGH CORRELATION 

Distinct1898
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.68450674
Minimum-1.2273194
Maximum2.3864536
Zeros30
Zeros (%)0.1%
Negative4656
Negative (%)12.4%
Memory size293.9 KiB
2024-05-20T00:02:13.328686image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-1.2273194
5-th percentile-0.52878088
Q10.26537639
median0.82993811
Q31.0931465
95-th percentile1.3620613
Maximum2.3864536
Range3.613773
Interquartile range (IQR)0.82777014

Descriptive statistics

Standard deviation0.58743672
Coefficient of variation (CV)0.85818983
Kurtosis0.14484869
Mean0.68450674
Median Absolute Deviation (MAD)0.3678022
Skewness-0.70184833
Sum25741.561
Variance0.3450819
MonotonicityNot monotonic
2024-05-20T00:02:13.501899image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.30016315 2282
 
6.1%
-0.7183076143 952
 
2.5%
0.2375717759 929
 
2.5%
1.07299602 911
 
2.4%
-0.2575258613 681
 
1.8%
1.05477047 488
 
1.3%
0.7411549687 434
 
1.2%
1.094812393 429
 
1.1%
0.629778266 428
 
1.1%
1.8322438 409
 
1.1%
Other values (1888) 29663
78.9%
ValueCountFrequency (%)
-1.22731936 5
 
< 0.1%
-1.160905719 3
 
< 0.1%
-1.153503895 2
 
< 0.1%
-1.063666463 1
 
< 0.1%
-0.9664271474 247
0.7%
-0.9565482736 3
 
< 0.1%
-0.893484056 125
0.3%
-0.890312314 2
 
< 0.1%
-0.8902192116 11
 
< 0.1%
-0.8835706115 39
 
0.1%
ValueCountFrequency (%)
2.386453629 4
 
< 0.1%
2.168669939 11
 
< 0.1%
2.016318083 147
 
0.4%
1.919445395 120
 
0.3%
1.8322438 409
1.1%
1.807303309 1
 
< 0.1%
1.776978254 23
 
0.1%
1.758986831 1
 
< 0.1%
1.757221937 2
 
< 0.1%
1.734416485 2
 
< 0.1%

exterior_color_x3
Real number (ℝ)

Distinct1901
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.40705956
Minimum-3.5100327
Maximum0.66745049
Zeros30
Zeros (%)0.1%
Negative30886
Negative (%)82.1%
Memory size293.9 KiB
2024-05-20T00:02:13.661456image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-3.5100327
5-th percentile-1.3930054
Q1-0.6146127
median-0.34153304
Q3-0.063247167
95-th percentile0.16928968
Maximum0.66745049
Range4.1774831
Interquartile range (IQR)0.55136553

Descriptive statistics

Standard deviation0.5039437
Coefficient of variation (CV)-1.2380097
Kurtosis1.2662654
Mean-0.40705956
Median Absolute Deviation (MAD)0.27828587
Skewness-0.87365066
Sum-15307.882
Variance0.25395925
MonotonicityNot monotonic
2024-05-20T00:02:13.811517image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.06324716657 2282
 
6.1%
0.1183137298 952
 
2.5%
0.1416909993 929
 
2.5%
-0.2078505009 911
 
2.4%
0.6487338543 681
 
1.8%
-1.367208958 488
 
1.3%
-0.5460887551 434
 
1.2%
-0.4349435866 429
 
1.1%
-0.7438818216 428
 
1.1%
-0.5880366564 409
 
1.1%
Other values (1891) 29663
78.9%
ValueCountFrequency (%)
-3.510032654 3
 
< 0.1%
-3.104436636 11
 
< 0.1%
-2.963784218 1
 
< 0.1%
-2.71809268 1
 
< 0.1%
-2.235822678 15
 
< 0.1%
-2.170943499 2
 
< 0.1%
-2.165496588 10
 
< 0.1%
-2.159054995 201
0.5%
-2.116509914 1
 
< 0.1%
-2.051227331 3
 
< 0.1%
ValueCountFrequency (%)
0.6674504876 2
 
< 0.1%
0.6487338543 681
1.8%
0.6393755674 24
 
0.1%
0.5860561132 5
 
< 0.1%
0.5817661285 1
 
< 0.1%
0.5564879775 1
 
< 0.1%
0.5485950708 210
 
0.6%
0.5377053022 20
 
0.1%
0.5104919076 113
 
0.3%
0.4996722639 21
 
0.1%

exterior_color_x4
Real number (ℝ)

HIGH CORRELATION 

Distinct1897
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.0504987
Minimum-3.2427816
Maximum0.19847639
Zeros30
Zeros (%)0.1%
Negative37375
Negative (%)99.4%
Memory size293.9 KiB
2024-05-20T00:02:13.985967image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-3.2427816
5-th percentile-2.2842441
Q1-1.4921011
median-0.88851881
Q3-0.47605655
95-th percentile-0.26058449
Maximum0.19847639
Range3.441258
Interquartile range (IQR)1.0160445

Descriptive statistics

Standard deviation0.65526324
Coefficient of variation (CV)-0.62376399
Kurtosis0.32278
Mean-1.0504987
Median Absolute Deviation (MAD)0.44773757
Skewness-0.87762716
Sum-39505.053
Variance0.42936991
MonotonicityNot monotonic
2024-05-20T00:02:14.158768image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.4289364517 2282
 
6.1%
-2.859434128 952
 
2.5%
-1.756619096 929
 
2.5%
-0.9457932711 911
 
2.4%
-2.24461627 681
 
1.8%
-0.4710775912 488
 
1.3%
-0.8093400598 434
 
1.2%
-0.4450545013 429
 
1.1%
-0.4509477317 428
 
1.1%
-1.575397968 409
 
1.1%
Other values (1887) 29663
78.9%
ValueCountFrequency (%)
-3.242781639 48
 
0.1%
-2.988175392 170
 
0.5%
-2.926416874 1
 
< 0.1%
-2.893342972 8
 
< 0.1%
-2.888619423 5
 
< 0.1%
-2.859434128 952
2.5%
-2.797168732 2
 
< 0.1%
-2.766561985 5
 
< 0.1%
-2.752726316 58
 
0.2%
-2.718497753 1
 
< 0.1%
ValueCountFrequency (%)
0.1984763891 2
 
< 0.1%
0.1844702512 7
 
< 0.1%
0.1816361398 1
 
< 0.1%
0.1806037724 1
 
< 0.1%
0.1727492064 1
 
< 0.1%
0.1634026617 1
 
< 0.1%
0.1547729522 22
0.1%
0.1407849342 1
 
< 0.1%
0.1399295032 1
 
< 0.1%
0.1376764029 1
 
< 0.1%

interior_color_x0
Real number (ℝ)

HIGH CORRELATION 

Distinct1005
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.47377412
Minimum-2.3731728
Maximum0.19349997
Zeros124
Zeros (%)0.3%
Negative35383
Negative (%)94.1%
Memory size293.9 KiB
2024-05-20T00:02:14.347728image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-2.3731728
5-th percentile-0.87139606
Q1-0.51743746
median-0.49426496
Q3-0.35561335
95-th percentile0.14623532
Maximum0.19349997
Range2.5666727
Interquartile range (IQR)0.16182411

Descriptive statistics

Standard deviation0.25116765
Coefficient of variation (CV)-0.53014218
Kurtosis8.170376
Mean-0.47377412
Median Absolute Deviation (MAD)0.076409936
Skewness-0.80543476
Sum-17816.75
Variance0.063085186
MonotonicityNot monotonic
2024-05-20T00:02:14.549051image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.4942649603 14959
39.8%
-0.3139528036 3031
 
8.1%
-0.517437458 2378
 
6.3%
-0.7491714954 2154
 
5.7%
0.146235317 1875
 
5.0%
-0.3314826488 1711
 
4.5%
-0.3556133509 909
 
2.4%
-0.4178550243 560
 
1.5%
-0.2350679487 503
 
1.3%
-1.061753273 394
 
1.0%
Other values (995) 9132
24.3%
ValueCountFrequency (%)
-2.37317276 74
0.2%
-2.330812693 2
 
< 0.1%
-1.696282983 6
 
< 0.1%
-1.683698058 1
 
< 0.1%
-1.663648725 3
 
< 0.1%
-1.640157223 60
0.2%
-1.605787754 1
 
< 0.1%
-1.527393103 1
 
< 0.1%
-1.451779842 6
 
< 0.1%
-1.370571852 16
 
< 0.1%
ValueCountFrequency (%)
0.1934999675 27
 
0.1%
0.1841681004 2
 
< 0.1%
0.1838356555 1
 
< 0.1%
0.1769794077 3
 
< 0.1%
0.174426958 1
 
< 0.1%
0.1719948053 2
 
< 0.1%
0.1622290909 1
 
< 0.1%
0.1616194546 2
 
< 0.1%
0.1474763155 7
 
< 0.1%
0.146235317 1875
5.0%

interior_color_x1
Real number (ℝ)

HIGH CORRELATION 

Distinct1006
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.46271089
Minimum-0.18563056
Maximum1.617914
Zeros124
Zeros (%)0.3%
Negative338
Negative (%)0.9%
Memory size293.9 KiB
2024-05-20T00:02:14.704234image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-0.18563056
5-th percentile0.10140524
Q10.38753486
median0.38753486
Q30.54801261
95-th percentile0.82123405
Maximum1.617914
Range1.8035445
Interquartile range (IQR)0.16047776

Descriptive statistics

Standard deviation0.22646634
Coefficient of variation (CV)0.48943377
Kurtosis1.7542784
Mean0.46271089
Median Absolute Deviation (MAD)0.11726177
Skewness0.89298021
Sum17400.706
Variance0.051287001
MonotonicityNot monotonic
2024-05-20T00:02:14.985948image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3875348568 14959
39.8%
0.5480126143 3031
 
8.1%
0.8212340474 2378
 
6.3%
0.7358144522 2154
 
5.7%
0.1014052406 1875
 
5.0%
0.2289542705 1711
 
4.5%
0.3029362559 909
 
2.4%
0.4055069983 560
 
1.5%
0.2473456115 503
 
1.3%
1.219044447 394
 
1.0%
Other values (996) 9132
24.3%
ValueCountFrequency (%)
-0.1856305599 2
 
< 0.1%
-0.1821084023 5
 
< 0.1%
-0.1731399596 1
 
< 0.1%
-0.1720187366 2
 
< 0.1%
-0.1697011441 1
 
< 0.1%
-0.1592723578 1
 
< 0.1%
-0.1557089537 27
0.1%
-0.153109625 1
 
< 0.1%
-0.1529581994 1
 
< 0.1%
-0.145032689 4
 
< 0.1%
ValueCountFrequency (%)
1.617913961 7
 
< 0.1%
1.490871191 74
 
0.2%
1.345086932 6
 
< 0.1%
1.267589569 11
 
< 0.1%
1.265928507 2
 
< 0.1%
1.264083266 2
 
< 0.1%
1.241563797 6
 
< 0.1%
1.219044447 394
1.0%
1.215440989 2
 
< 0.1%
1.209338069 1
 
< 0.1%

interior_color_x2
Real number (ℝ)

HIGH CORRELATION 

Distinct1007
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.58657566
Minimum-0.19890115
Maximum1.6741534
Zeros124
Zeros (%)0.3%
Negative317
Negative (%)0.8%
Memory size293.9 KiB
2024-05-20T00:02:15.202469image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-0.19890115
5-th percentile0.13515386
Q10.3568562
median0.5897873
Q30.5897873
95-th percentile1.2861211
Maximum1.6741534
Range1.8730546
Interquartile range (IQR)0.23293111

Descriptive statistics

Standard deviation0.3028972
Coefficient of variation (CV)0.51638216
Kurtosis0.54138951
Mean0.58657566
Median Absolute Deviation (MAD)0.16120607
Skewness0.7160801
Sum22058.764
Variance0.091746716
MonotonicityNot monotonic
2024-05-20T00:02:15.377670image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5897873044 14959
39.8%
0.3238786161 3031
 
8.1%
0.9810428023 2378
 
6.3%
1.28612113 2154
 
5.7%
0.1351538599 1875
 
5.0%
0.3568561971 1711
 
4.5%
0.1501284093 909
 
2.4%
0.2745200992 560
 
1.5%
0.3745622039 503
 
1.3%
0.4491575062 394
 
1.0%
Other values (997) 9132
24.3%
ValueCountFrequency (%)
-0.1989011467 27
0.1%
-0.1926358491 1
 
< 0.1%
-0.1918946207 2
 
< 0.1%
-0.1850786656 1
 
< 0.1%
-0.1691945791 2
 
< 0.1%
-0.1667596102 2
 
< 0.1%
-0.157146126 2
 
< 0.1%
-0.1557633877 1
 
< 0.1%
-0.1498440206 2
 
< 0.1%
-0.1477413625 1
 
< 0.1%
ValueCountFrequency (%)
1.674153447 7
 
< 0.1%
1.663210392 11
 
< 0.1%
1.644211531 46
0.1%
1.569934249 7
 
< 0.1%
1.5572685 1
 
< 0.1%
1.524847984 4
 
< 0.1%
1.520518303 30
0.1%
1.484605789 2
 
< 0.1%
1.480137348 24
0.1%
1.472362518 1
 
< 0.1%

interior_color_x3
Real number (ℝ)

HIGH CORRELATION 

Distinct1006
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.018644563
Minimum-1.3000717
Maximum0.33831894
Zeros124
Zeros (%)0.3%
Negative14624
Negative (%)38.9%
Memory size293.9 KiB
2024-05-20T00:02:15.612821image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-1.3000717
5-th percentile-0.67214864
Q1-0.32673895
median0.080759585
Q30.33831894
95-th percentile0.33831894
Maximum0.33831894
Range1.6383907
Interquartile range (IQR)0.6650579

Descriptive statistics

Standard deviation0.36924141
Coefficient of variation (CV)-19.80424
Kurtosis-0.96136982
Mean-0.018644563
Median Absolute Deviation (MAD)0.25755936
Skewness-0.6166468
Sum-701.14745
Variance0.13633922
MonotonicityNot monotonic
2024-05-20T00:02:15.876506image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.338318944 14959
39.8%
0.0807595849 3031
 
8.1%
-0.6057642102 2378
 
6.3%
-0.6721486449 2154
 
5.7%
0.01525731105 1875
 
5.0%
-0.3267389536 1711
 
4.5%
0.1106414646 909
 
2.4%
0.08260063827 560
 
1.5%
-0.2243861854 503
 
1.3%
-0.3761080205 394
 
1.0%
Other values (996) 9132
24.3%
ValueCountFrequency (%)
-1.300071716 2
 
< 0.1%
-1.109388828 4
 
< 0.1%
-1.014641404 1
 
< 0.1%
-1.009524941 2
 
< 0.1%
-0.9533045888 4
 
< 0.1%
-0.9446456432 1
 
< 0.1%
-0.9407006502 46
0.1%
-0.9355012774 7
 
< 0.1%
-0.9269840717 11
 
< 0.1%
-0.9198940992 74
0.2%
ValueCountFrequency (%)
0.338318944 14959
39.8%
0.2751825452 18
 
< 0.1%
0.2697351873 2
 
< 0.1%
0.2643211782 2
 
< 0.1%
0.2388735563 10
 
< 0.1%
0.2378211021 6
 
< 0.1%
0.2348012477 14
 
< 0.1%
0.2329705656 3
 
< 0.1%
0.2253863811 1
 
< 0.1%
0.2184856087 8
 
< 0.1%

interior_color_x4
Real number (ℝ)

HIGH CORRELATION 

Distinct1006
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.18183252
Minimum-0.49870196
Maximum0.81330609
Zeros124
Zeros (%)0.3%
Negative23633
Negative (%)62.8%
Memory size293.9 KiB
2024-05-20T00:02:16.034625image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-0.49870196
5-th percentile-0.49870196
Q1-0.49870196
median-0.19450934
Q30.055999383
95-th percentile0.25908411
Maximum0.81330609
Range1.3120081
Interquartile range (IQR)0.55470134

Descriptive statistics

Standard deviation0.29525218
Coefficient of variation (CV)-1.623759
Kurtosis-0.91806617
Mean-0.18183252
Median Absolute Deviation (MAD)0.30419262
Skewness0.33971558
Sum-6837.9936
Variance0.087173852
MonotonicityNot monotonic
2024-05-20T00:02:16.279870image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.4987019598 14959
39.8%
-0.1945093423 3031
 
8.1%
0.1044528782 2378
 
6.3%
0.05599938333 2154
 
5.7%
0.1270178109 1875
 
5.0%
0.01482179761 1711
 
4.5%
-0.2170253694 909
 
2.4%
-0.08903948963 560
 
1.5%
0.1336151361 503
 
1.3%
-0.05107550323 394
 
1.0%
Other values (996) 9132
24.3%
ValueCountFrequency (%)
-0.4987019598 14959
39.8%
-0.3876874447 1
 
< 0.1%
-0.3491453528 1
 
< 0.1%
-0.3234826028 24
 
0.1%
-0.3229342401 1
 
< 0.1%
-0.3228434324 118
 
0.3%
-0.3218908906 1
 
< 0.1%
-0.3077826202 3
 
< 0.1%
-0.3023605645 2
 
< 0.1%
-0.2900065184 2
 
< 0.1%
ValueCountFrequency (%)
0.8133060932 1
 
< 0.1%
0.7796087861 60
0.2%
0.7773840427 2
 
< 0.1%
0.7301509976 2
 
< 0.1%
0.7077647448 2
 
< 0.1%
0.7020905018 1
 
< 0.1%
0.6245722771 4
 
< 0.1%
0.6023901701 99
0.3%
0.5960175991 1
 
< 0.1%
0.5875912905 45
0.1%

drivetrain_All-wheel Drive
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
1.0
28703 
0.0
8903 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters112818
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 28703
76.3%
0.0 8903
 
23.7%

Length

2024-05-20T00:02:16.455031image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:16.595145image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 28703
76.3%
0.0 8903
 
23.7%

Most occurring characters

ValueCountFrequency (%)
0 46509
41.2%
. 37606
33.3%
1 28703
25.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 46509
41.2%
. 37606
33.3%
1 28703
25.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 46509
41.2%
. 37606
33.3%
1 28703
25.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 46509
41.2%
. 37606
33.3%
1 28703
25.4%

drivetrain_Front-wheel Drive
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
30286 
1.0
7320 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters112818
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 30286
80.5%
1.0 7320
 
19.5%

Length

2024-05-20T00:02:16.805110image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:16.923971image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 30286
80.5%
1.0 7320
 
19.5%

Most occurring characters

ValueCountFrequency (%)
0 67892
60.2%
. 37606
33.3%
1 7320
 
6.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 67892
60.2%
. 37606
33.3%
1 7320
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 67892
60.2%
. 37606
33.3%
1 7320
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 67892
60.2%
. 37606
33.3%
1 7320
 
6.5%

drivetrain_Rear-wheel Drive
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
36023 
1.0
 
1583

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters112818
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 36023
95.8%
1.0 1583
 
4.2%

Length

2024-05-20T00:02:17.117190image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:17.242292image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 36023
95.8%
1.0 1583
 
4.2%

Most occurring characters

ValueCountFrequency (%)
0 73629
65.3%
. 37606
33.3%
1 1583
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 73629
65.3%
. 37606
33.3%
1 1583
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 73629
65.3%
. 37606
33.3%
1 1583
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 73629
65.3%
. 37606
33.3%
1 1583
 
1.4%

make_Acura
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
36842 
1.0
 
764

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters112818
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 36842
98.0%
1.0 764
 
2.0%

Length

2024-05-20T00:02:17.453200image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:17.602401image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 36842
98.0%
1.0 764
 
2.0%

Most occurring characters

ValueCountFrequency (%)
0 74448
66.0%
. 37606
33.3%
1 764
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 74448
66.0%
. 37606
33.3%
1 764
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 74448
66.0%
. 37606
33.3%
1 764
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 74448
66.0%
. 37606
33.3%
1 764
 
0.7%

make_Audi
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
36356 
1.0
 
1250

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters112818
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 36356
96.7%
1.0 1250
 
3.3%

Length

2024-05-20T00:02:17.796268image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:17.914613image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 36356
96.7%
1.0 1250
 
3.3%

Most occurring characters

ValueCountFrequency (%)
0 73962
65.6%
. 37606
33.3%
1 1250
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 73962
65.6%
. 37606
33.3%
1 1250
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 73962
65.6%
. 37606
33.3%
1 1250
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 73962
65.6%
. 37606
33.3%
1 1250
 
1.1%

make_BMW
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
35744 
1.0
 
1862

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters112818
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 35744
95.0%
1.0 1862
 
5.0%

Length

2024-05-20T00:02:18.073920image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:18.243054image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 35744
95.0%
1.0 1862
 
5.0%

Most occurring characters

ValueCountFrequency (%)
0 73350
65.0%
. 37606
33.3%
1 1862
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 73350
65.0%
. 37606
33.3%
1 1862
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 73350
65.0%
. 37606
33.3%
1 1862
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 73350
65.0%
. 37606
33.3%
1 1862
 
1.7%

make_Buick
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
37142 
1.0
 
464

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters112818
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 37142
98.8%
1.0 464
 
1.2%

Length

2024-05-20T00:02:18.394811image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:18.509109image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 37142
98.8%
1.0 464
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 74748
66.3%
. 37606
33.3%
1 464
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 74748
66.3%
. 37606
33.3%
1 464
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 74748
66.3%
. 37606
33.3%
1 464
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 74748
66.3%
. 37606
33.3%
1 464
 
0.4%

make_Cadillac
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
36409 
1.0
 
1197

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters112818
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 36409
96.8%
1.0 1197
 
3.2%

Length

2024-05-20T00:02:18.633378image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:18.764982image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 36409
96.8%
1.0 1197
 
3.2%

Most occurring characters

ValueCountFrequency (%)
0 74015
65.6%
. 37606
33.3%
1 1197
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 74015
65.6%
. 37606
33.3%
1 1197
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 74015
65.6%
. 37606
33.3%
1 1197
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 74015
65.6%
. 37606
33.3%
1 1197
 
1.1%

make_Chevrolet
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
33891 
1.0
3715 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters112818
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 33891
90.1%
1.0 3715
 
9.9%

Length

2024-05-20T00:02:18.917772image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:19.108776image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 33891
90.1%
1.0 3715
 
9.9%

Most occurring characters

ValueCountFrequency (%)
0 71497
63.4%
. 37606
33.3%
1 3715
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 71497
63.4%
. 37606
33.3%
1 3715
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 71497
63.4%
. 37606
33.3%
1 3715
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 71497
63.4%
. 37606
33.3%
1 3715
 
3.3%

make_Dodge
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
36536 
1.0
 
1070

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters112818
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 36536
97.2%
1.0 1070
 
2.8%

Length

2024-05-20T00:02:19.346474image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:19.457358image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 36536
97.2%
1.0 1070
 
2.8%

Most occurring characters

ValueCountFrequency (%)
0 74142
65.7%
. 37606
33.3%
1 1070
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 74142
65.7%
. 37606
33.3%
1 1070
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 74142
65.7%
. 37606
33.3%
1 1070
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 74142
65.7%
. 37606
33.3%
1 1070
 
0.9%

make_Ford
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
34149 
1.0
3457 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters112818
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 34149
90.8%
1.0 3457
 
9.2%

Length

2024-05-20T00:02:19.812539image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:19.993948image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 34149
90.8%
1.0 3457
 
9.2%

Most occurring characters

ValueCountFrequency (%)
0 71755
63.6%
. 37606
33.3%
1 3457
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 71755
63.6%
. 37606
33.3%
1 3457
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 71755
63.6%
. 37606
33.3%
1 3457
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 71755
63.6%
. 37606
33.3%
1 3457
 
3.1%

make_GMC
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
36748 
1.0
 
858

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters112818
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 36748
97.7%
1.0 858
 
2.3%

Length

2024-05-20T00:02:20.137169image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:20.250551image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 36748
97.7%
1.0 858
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 74354
65.9%
. 37606
33.3%
1 858
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 74354
65.9%
. 37606
33.3%
1 858
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 74354
65.9%
. 37606
33.3%
1 858
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 74354
65.9%
. 37606
33.3%
1 858
 
0.8%

make_Honda
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
36286 
1.0
 
1320

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters112818
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 36286
96.5%
1.0 1320
 
3.5%

Length

2024-05-20T00:02:20.375465image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:20.481752image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 36286
96.5%
1.0 1320
 
3.5%

Most occurring characters

ValueCountFrequency (%)
0 73892
65.5%
. 37606
33.3%
1 1320
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 73892
65.5%
. 37606
33.3%
1 1320
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 73892
65.5%
. 37606
33.3%
1 1320
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 73892
65.5%
. 37606
33.3%
1 1320
 
1.2%

make_Hyundai
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
35178 
1.0
 
2428

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters112818
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 35178
93.5%
1.0 2428
 
6.5%

Length

2024-05-20T00:02:20.608766image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:20.774891image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 35178
93.5%
1.0 2428
 
6.5%

Most occurring characters

ValueCountFrequency (%)
0 72784
64.5%
. 37606
33.3%
1 2428
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 72784
64.5%
. 37606
33.3%
1 2428
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 72784
64.5%
. 37606
33.3%
1 2428
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 72784
64.5%
. 37606
33.3%
1 2428
 
2.2%

make_INFINITI
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
37023 
1.0
 
583

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters112818
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 37023
98.4%
1.0 583
 
1.6%

Length

2024-05-20T00:02:20.896241image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:21.090583image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 37023
98.4%
1.0 583
 
1.6%

Most occurring characters

ValueCountFrequency (%)
0 74629
66.1%
. 37606
33.3%
1 583
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 74629
66.1%
. 37606
33.3%
1 583
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 74629
66.1%
. 37606
33.3%
1 583
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 74629
66.1%
. 37606
33.3%
1 583
 
0.5%

make_Jeep
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
34624 
1.0
 
2982

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters112818
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 34624
92.1%
1.0 2982
 
7.9%

Length

2024-05-20T00:02:21.278449image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:21.398536image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 34624
92.1%
1.0 2982
 
7.9%

Most occurring characters

ValueCountFrequency (%)
0 72230
64.0%
. 37606
33.3%
1 2982
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 72230
64.0%
. 37606
33.3%
1 2982
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 72230
64.0%
. 37606
33.3%
1 2982
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 72230
64.0%
. 37606
33.3%
1 2982
 
2.6%

make_Kia
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
36440 
1.0
 
1166

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters112818
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 36440
96.9%
1.0 1166
 
3.1%

Length

2024-05-20T00:02:21.526886image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:21.636720image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 36440
96.9%
1.0 1166
 
3.1%

Most occurring characters

ValueCountFrequency (%)
0 74046
65.6%
. 37606
33.3%
1 1166
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 74046
65.6%
. 37606
33.3%
1 1166
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 74046
65.6%
. 37606
33.3%
1 1166
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 74046
65.6%
. 37606
33.3%
1 1166
 
1.0%

make_Land Rover
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
37136 
1.0
 
470

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters112818
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 37136
98.8%
1.0 470
 
1.2%

Length

2024-05-20T00:02:21.760659image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:21.989024image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 37136
98.8%
1.0 470
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 74742
66.3%
. 37606
33.3%
1 470
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 74742
66.3%
. 37606
33.3%
1 470
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 74742
66.3%
. 37606
33.3%
1 470
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 74742
66.3%
. 37606
33.3%
1 470
 
0.4%

make_Lexus
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
36839 
1.0
 
767

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters112818
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 36839
98.0%
1.0 767
 
2.0%

Length

2024-05-20T00:02:22.139499image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:22.252806image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 36839
98.0%
1.0 767
 
2.0%

Most occurring characters

ValueCountFrequency (%)
0 74445
66.0%
. 37606
33.3%
1 767
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 74445
66.0%
. 37606
33.3%
1 767
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 74445
66.0%
. 37606
33.3%
1 767
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 74445
66.0%
. 37606
33.3%
1 767
 
0.7%

make_Lincoln
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
36888 
1.0
 
718

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters112818
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 36888
98.1%
1.0 718
 
1.9%

Length

2024-05-20T00:02:22.378228image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:22.488112image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 36888
98.1%
1.0 718
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 74494
66.0%
. 37606
33.3%
1 718
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 74494
66.0%
. 37606
33.3%
1 718
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 74494
66.0%
. 37606
33.3%
1 718
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 74494
66.0%
. 37606
33.3%
1 718
 
0.6%

make_Mazda
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
36349 
1.0
 
1257

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters112818
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 36349
96.7%
1.0 1257
 
3.3%

Length

2024-05-20T00:02:22.612652image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:22.731613image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 36349
96.7%
1.0 1257
 
3.3%

Most occurring characters

ValueCountFrequency (%)
0 73955
65.6%
. 37606
33.3%
1 1257
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 73955
65.6%
. 37606
33.3%
1 1257
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 73955
65.6%
. 37606
33.3%
1 1257
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 73955
65.6%
. 37606
33.3%
1 1257
 
1.1%

make_Mercedes-Benz
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
35141 
1.0
 
2465

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters112818
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 35141
93.4%
1.0 2465
 
6.6%

Length

2024-05-20T00:02:22.876432image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:22.985622image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 35141
93.4%
1.0 2465
 
6.6%

Most occurring characters

ValueCountFrequency (%)
0 72747
64.5%
. 37606
33.3%
1 2465
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 72747
64.5%
. 37606
33.3%
1 2465
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 72747
64.5%
. 37606
33.3%
1 2465
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 72747
64.5%
. 37606
33.3%
1 2465
 
2.2%

make_Nissan
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
35106 
1.0
 
2500

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters112818
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 35106
93.4%
1.0 2500
 
6.6%

Length

2024-05-20T00:02:23.111135image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:23.220471image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 35106
93.4%
1.0 2500
 
6.6%

Most occurring characters

ValueCountFrequency (%)
0 72712
64.5%
. 37606
33.3%
1 2500
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 72712
64.5%
. 37606
33.3%
1 2500
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 72712
64.5%
. 37606
33.3%
1 2500
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 72712
64.5%
. 37606
33.3%
1 2500
 
2.2%

make_Porsche
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
37405 
1.0
 
201

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters112818
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 37405
99.5%
1.0 201
 
0.5%

Length

2024-05-20T00:02:23.341406image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:23.450722image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 37405
99.5%
1.0 201
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 75011
66.5%
. 37606
33.3%
1 201
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 75011
66.5%
. 37606
33.3%
1 201
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 75011
66.5%
. 37606
33.3%
1 201
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 75011
66.5%
. 37606
33.3%
1 201
 
0.2%

make_RAM
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
37004 
1.0
 
602

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters112818
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 37004
98.4%
1.0 602
 
1.6%

Length

2024-05-20T00:02:23.574636image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:23.687520image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 37004
98.4%
1.0 602
 
1.6%

Most occurring characters

ValueCountFrequency (%)
0 74610
66.1%
. 37606
33.3%
1 602
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 74610
66.1%
. 37606
33.3%
1 602
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 74610
66.1%
. 37606
33.3%
1 602
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 74610
66.1%
. 37606
33.3%
1 602
 
0.5%

make_Subaru
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
35952 
1.0
 
1654

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters112818
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 35952
95.6%
1.0 1654
 
4.4%

Length

2024-05-20T00:02:23.806719image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:23.918280image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 35952
95.6%
1.0 1654
 
4.4%

Most occurring characters

ValueCountFrequency (%)
0 73558
65.2%
. 37606
33.3%
1 1654
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 73558
65.2%
. 37606
33.3%
1 1654
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 73558
65.2%
. 37606
33.3%
1 1654
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 73558
65.2%
. 37606
33.3%
1 1654
 
1.5%

make_Toyota
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
36303 
1.0
 
1303

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters112818
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 36303
96.5%
1.0 1303
 
3.5%

Length

2024-05-20T00:02:24.038033image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:24.201439image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 36303
96.5%
1.0 1303
 
3.5%

Most occurring characters

ValueCountFrequency (%)
0 73909
65.5%
. 37606
33.3%
1 1303
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 73909
65.5%
. 37606
33.3%
1 1303
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 73909
65.5%
. 37606
33.3%
1 1303
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 73909
65.5%
. 37606
33.3%
1 1303
 
1.2%

make_Volkswagen
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
35402 
1.0
 
2204

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters112818
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 35402
94.1%
1.0 2204
 
5.9%

Length

2024-05-20T00:02:24.366065image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:24.478456image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 35402
94.1%
1.0 2204
 
5.9%

Most occurring characters

ValueCountFrequency (%)
0 73008
64.7%
. 37606
33.3%
1 2204
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 73008
64.7%
. 37606
33.3%
1 2204
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 73008
64.7%
. 37606
33.3%
1 2204
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 73008
64.7%
. 37606
33.3%
1 2204
 
2.0%

make_Volvo
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
37257 
1.0
 
349

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters112818
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 37257
99.1%
1.0 349
 
0.9%

Length

2024-05-20T00:02:24.594840image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:24.707198image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 37257
99.1%
1.0 349
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 74863
66.4%
. 37606
33.3%
1 349
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 74863
66.4%
. 37606
33.3%
1 349
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 74863
66.4%
. 37606
33.3%
1 349
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 74863
66.4%
. 37606
33.3%
1 349
 
0.3%

bodystyle_Cargo Van
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
37231 
1.0
 
375

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters112818
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 37231
99.0%
1.0 375
 
1.0%

Length

2024-05-20T00:02:24.954182image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:25.071094image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 37231
99.0%
1.0 375
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0 74837
66.3%
. 37606
33.3%
1 375
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 74837
66.3%
. 37606
33.3%
1 375
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 74837
66.3%
. 37606
33.3%
1 375
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 74837
66.3%
. 37606
33.3%
1 375
 
0.3%

bodystyle_Convertible
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
37336 
1.0
 
270

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters112818
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 37336
99.3%
1.0 270
 
0.7%

Length

2024-05-20T00:02:25.188026image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:25.304526image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 37336
99.3%
1.0 270
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 74942
66.4%
. 37606
33.3%
1 270
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 74942
66.4%
. 37606
33.3%
1 270
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 74942
66.4%
. 37606
33.3%
1 270
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 74942
66.4%
. 37606
33.3%
1 270
 
0.2%

bodystyle_Coupe
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
36556 
1.0
 
1050

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters112818
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 36556
97.2%
1.0 1050
 
2.8%

Length

2024-05-20T00:02:25.426001image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:25.541347image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 36556
97.2%
1.0 1050
 
2.8%

Most occurring characters

ValueCountFrequency (%)
0 74162
65.7%
. 37606
33.3%
1 1050
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 74162
65.7%
. 37606
33.3%
1 1050
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 74162
65.7%
. 37606
33.3%
1 1050
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 74162
65.7%
. 37606
33.3%
1 1050
 
0.9%

bodystyle_Hatchback
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
37221 
1.0
 
385

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters112818
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 37221
99.0%
1.0 385
 
1.0%

Length

2024-05-20T00:02:25.660549image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:25.773910image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 37221
99.0%
1.0 385
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0 74827
66.3%
. 37606
33.3%
1 385
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 74827
66.3%
. 37606
33.3%
1 385
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 74827
66.3%
. 37606
33.3%
1 385
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 74827
66.3%
. 37606
33.3%
1 385
 
0.3%

bodystyle_Minivan
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
37571 
1.0
 
35

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters112818
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 37571
99.9%
1.0 35
 
0.1%

Length

2024-05-20T00:02:25.930002image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:26.066028image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 37571
99.9%
1.0 35
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 75177
66.6%
. 37606
33.3%
1 35
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 75177
66.6%
. 37606
33.3%
1 35
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 75177
66.6%
. 37606
33.3%
1 35
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 75177
66.6%
. 37606
33.3%
1 35
 
< 0.1%

bodystyle_Passenger Van
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
37454 
1.0
 
152

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters112818
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 37454
99.6%
1.0 152
 
0.4%

Length

2024-05-20T00:02:26.194240image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:26.335811image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 37454
99.6%
1.0 152
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 75060
66.5%
. 37606
33.3%
1 152
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 75060
66.5%
. 37606
33.3%
1 152
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 75060
66.5%
. 37606
33.3%
1 152
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 75060
66.5%
. 37606
33.3%
1 152
 
0.1%

bodystyle_Pickup Truck
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
34422 
1.0
 
3184

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters112818
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 34422
91.5%
1.0 3184
 
8.5%

Length

2024-05-20T00:02:26.532359image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:26.648433image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 34422
91.5%
1.0 3184
 
8.5%

Most occurring characters

ValueCountFrequency (%)
0 72028
63.8%
. 37606
33.3%
1 3184
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 72028
63.8%
. 37606
33.3%
1 3184
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 72028
63.8%
. 37606
33.3%
1 3184
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 72028
63.8%
. 37606
33.3%
1 3184
 
2.8%

bodystyle_SUV
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
1.0
25723 
0.0
11883 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters112818
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 25723
68.4%
0.0 11883
31.6%

Length

2024-05-20T00:02:26.778078image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:26.931878image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 25723
68.4%
0.0 11883
31.6%

Most occurring characters

ValueCountFrequency (%)
0 49489
43.9%
. 37606
33.3%
1 25723
22.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 49489
43.9%
. 37606
33.3%
1 25723
22.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 49489
43.9%
. 37606
33.3%
1 25723
22.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 49489
43.9%
. 37606
33.3%
1 25723
22.8%

bodystyle_Sedan
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
31412 
1.0
6194 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters112818
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 31412
83.5%
1.0 6194
 
16.5%

Length

2024-05-20T00:02:27.074374image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:27.184073image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 31412
83.5%
1.0 6194
 
16.5%

Most occurring characters

ValueCountFrequency (%)
0 69018
61.2%
. 37606
33.3%
1 6194
 
5.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 69018
61.2%
. 37606
33.3%
1 6194
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 69018
61.2%
. 37606
33.3%
1 6194
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 69018
61.2%
. 37606
33.3%
1 6194
 
5.5%

bodystyle_Wagon
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
37475 
1.0
 
131

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters112818
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 37475
99.7%
1.0 131
 
0.3%

Length

2024-05-20T00:02:27.317881image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:27.431202image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 37475
99.7%
1.0 131
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 75081
66.6%
. 37606
33.3%
1 131
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 75081
66.6%
. 37606
33.3%
1 131
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 75081
66.6%
. 37606
33.3%
1 131
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 75081
66.6%
. 37606
33.3%
1 131
 
0.1%

bodystyle_nan
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
37499 
1.0
 
107

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters112818
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 37499
99.7%
1.0 107
 
0.3%

Length

2024-05-20T00:02:27.559019image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:27.685550image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 37499
99.7%
1.0 107
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 75105
66.6%
. 37606
33.3%
1 107
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 75105
66.6%
. 37606
33.3%
1 107
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 75105
66.6%
. 37606
33.3%
1 107
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 75105
66.6%
. 37606
33.3%
1 107
 
0.1%

cat_x0
Real number (ℝ)

HIGH CORRELATION 

Distinct35
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.19077421
Minimum-0.6724633
Maximum1.0948846
Zeros0
Zeros (%)0.0%
Negative7671
Negative (%)20.4%
Memory size293.9 KiB
2024-05-20T00:02:27.838646image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-0.6724633
5-th percentile-0.33562684
Q10.030993938
median0.12328106
Q30.2258312
95-th percentile1.0166872
Maximum1.0948846
Range1.7673479
Interquartile range (IQR)0.19483726

Descriptive statistics

Standard deviation0.33535404
Coefficient of variation (CV)1.7578584
Kurtosis1.2986083
Mean0.19077421
Median Absolute Deviation (MAD)0.099178717
Skewness0.9591552
Sum7174.2548
Variance0.11246233
MonotonicityNot monotonic
2024-05-20T00:02:27.999745image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0.1232810616 8367
22.2%
0.03099393845 5523
14.7%
-0.01229947805 4104
10.9%
0.401217401 3338
 
8.9%
0.2224597782 2306
 
6.1%
0.1999615133 1992
 
5.3%
1.016687155 1679
 
4.5%
0.06438097358 1662
 
4.4%
-0.4631993473 1258
 
3.3%
0.8305656314 1167
 
3.1%
Other values (25) 6210
16.5%
ValueCountFrequency (%)
-0.6724632978 86
 
0.2%
-0.4631993473 1258
 
3.3%
-0.3356268406 1008
 
2.7%
-0.2919861972 554
 
1.5%
-0.2653982937 65
 
0.2%
-0.2560895979 138
 
0.4%
-0.05258842185 109
 
0.3%
-0.0498467274 171
 
0.5%
-0.0263671279 178
 
0.5%
-0.01229947805 4104
10.9%
ValueCountFrequency (%)
1.094884634 868
2.3%
1.016687155 1679
4.5%
0.9313512444 35
 
0.1%
0.9192990065 21
 
0.1%
0.8305656314 1167
3.1%
0.7679092288 62
 
0.2%
0.7367572188 476
 
1.3%
0.6361443996 97
 
0.3%
0.5120633245 14
 
< 0.1%
0.4917612076 120
 
0.3%

cat_x1
Real number (ℝ)

HIGH CORRELATION 

Distinct35
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.39644773
Minimum-1.9109991
Maximum0.30777144
Zeros0
Zeros (%)0.0%
Negative37215
Negative (%)99.0%
Memory size293.9 KiB
2024-05-20T00:02:28.152449image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-1.9109991
5-th percentile-0.86696702
Q1-0.38160509
median-0.35996246
Q3-0.26118255
95-th percentile-0.23218849
Maximum0.30777144
Range2.2187705
Interquartile range (IQR)0.12042254

Descriptive statistics

Standard deviation0.21295085
Coefficient of variation (CV)-0.53714735
Kurtosis6.4058537
Mean-0.39644773
Median Absolute Deviation (MAD)0.061425865
Skewness-1.5724674
Sum-14908.813
Variance0.045348063
MonotonicityNot monotonic
2024-05-20T00:02:28.299532image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
-0.3816050887 8367
22.2%
-0.2985365987 5523
14.7%
-0.232188493 4104
10.9%
-0.2376151383 3338
 
8.9%
-0.613470912 2306
 
6.1%
-0.5093790293 1992
 
5.3%
-0.8669670224 1679
 
4.5%
-0.3599624634 1662
 
4.4%
-0.3030163944 1258
 
3.3%
-0.7890874743 1167
 
3.1%
Other values (25) 6210
16.5%
ValueCountFrequency (%)
-1.91099906 35
 
0.1%
-1.809605837 62
 
0.2%
-1.520576596 17
 
< 0.1%
-1.110056043 120
 
0.3%
-0.8875585794 476
 
1.3%
-0.8669670224 1679
4.5%
-0.7890874743 1167
3.1%
-0.7393200397 868
2.3%
-0.7229655385 97
 
0.3%
-0.6710458398 14
 
< 0.1%
ValueCountFrequency (%)
0.3077714443 261
 
0.7%
0.2529188097 21
 
0.1%
0.01071238518 109
 
0.3%
-0.05026694015 138
 
0.4%
-0.1463941783 554
 
1.5%
-0.232188493 4104
10.9%
-0.2376151383 3338
8.9%
-0.2611825466 1008
 
2.7%
-0.2985365987 5523
14.7%
-0.299779892 171
 
0.5%

cat_x2
Real number (ℝ)

HIGH CORRELATION 

Distinct35
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.84585139
Minimum-0.27193058
Maximum2.6237638
Zeros0
Zeros (%)0.0%
Negative924
Negative (%)2.5%
Memory size293.9 KiB
2024-05-20T00:02:28.434268image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-0.27193058
5-th percentile0.2573179
Q10.74373335
median0.7503739
Q30.88927448
95-th percentile1.4716611
Maximum2.6237638
Range2.8956944
Interquartile range (IQR)0.14554113

Descriptive statistics

Standard deviation0.32237177
Coefficient of variation (CV)0.38112105
Kurtosis3.4454284
Mean0.84585139
Median Absolute Deviation (MAD)0.061797082
Skewness0.49445913
Sum31809.087
Variance0.10392356
MonotonicityNot monotonic
2024-05-20T00:02:28.601070image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0.7437333465 8367
22.2%
0.8121709824 5523
14.7%
0.889274478 4104
10.9%
0.7503738999 3338
 
8.9%
0.9181630611 2306
 
6.1%
0.5571174026 1992
 
5.3%
1.471661091 1679
 
4.5%
0.702658534 1662
 
4.4%
1.513098717 1258
 
3.3%
1.447463393 1167
 
3.1%
Other values (25) 6210
16.5%
ValueCountFrequency (%)
-0.2719305754 261
 
0.7%
-0.1380209923 109
 
0.3%
-0.001169999479 554
 
1.5%
0.08232649416 138
 
0.4%
0.2573179007 868
2.3%
0.4859781563 17
 
< 0.1%
0.5571174026 1992
5.3%
0.6247327924 24
 
0.1%
0.6732704043 993
2.6%
0.7021681666 368
 
1.0%
ValueCountFrequency (%)
2.6237638 35
 
0.1%
2.56569767 62
 
0.2%
1.594357252 178
 
0.5%
1.513098717 1258
3.3%
1.471661091 1679
4.5%
1.465922356 21
 
0.1%
1.447463393 1167
3.1%
1.317383528 120
 
0.3%
1.265677929 1008
2.7%
1.234428525 97
 
0.3%

fuel_type_Electric
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
36290 
1.0
 
1316

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters112818
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 36290
96.5%
1.0 1316
 
3.5%

Length

2024-05-20T00:02:28.839072image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:28.945709image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 36290
96.5%
1.0 1316
 
3.5%

Most occurring characters

ValueCountFrequency (%)
0 73896
65.5%
. 37606
33.3%
1 1316
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 73896
65.5%
. 37606
33.3%
1 1316
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 73896
65.5%
. 37606
33.3%
1 1316
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 73896
65.5%
. 37606
33.3%
1 1316
 
1.2%

fuel_type_Flexible
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
37470 
1.0
 
136

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters112818
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 37470
99.6%
1.0 136
 
0.4%

Length

2024-05-20T00:02:29.216566image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:29.330102image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 37470
99.6%
1.0 136
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 75076
66.5%
. 37606
33.3%
1 136
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 75076
66.5%
. 37606
33.3%
1 136
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 75076
66.5%
. 37606
33.3%
1 136
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 75076
66.5%
. 37606
33.3%
1 136
 
0.1%

fuel_type_Gasoline
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
1.0
34798 
0.0
 
2808

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters112818
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 34798
92.5%
0.0 2808
 
7.5%

Length

2024-05-20T00:02:29.447233image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:29.557917image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 34798
92.5%
0.0 2808
 
7.5%

Most occurring characters

ValueCountFrequency (%)
0 40414
35.8%
. 37606
33.3%
1 34798
30.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 40414
35.8%
. 37606
33.3%
1 34798
30.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 40414
35.8%
. 37606
33.3%
1 34798
30.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 40414
35.8%
. 37606
33.3%
1 34798
30.8%

fuel_type_Hybrid
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.9 KiB
0.0
36250 
1.0
 
1356

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters112818
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 36250
96.4%
1.0 1356
 
3.6%

Length

2024-05-20T00:02:29.703947image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:29.838451image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 36250
96.4%
1.0 1356
 
3.6%

Most occurring characters

ValueCountFrequency (%)
0 73856
65.5%
. 37606
33.3%
1 1356
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 73856
65.5%
. 37606
33.3%
1 1356
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 73856
65.5%
. 37606
33.3%
1 1356
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 73856
65.5%
. 37606
33.3%
1 1356
 
1.2%

Interactions

2024-05-20T00:01:51.838754image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:19.383948image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:21.676122image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:23.783332image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:25.844504image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:27.893994image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:30.374656image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:32.395891image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:34.433330image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:36.702695image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:38.959275image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:41.104940image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:43.240821image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:45.456331image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:47.955989image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:49.863492image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:51.947281image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:19.506149image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:21.805391image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:23.926827image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:25.959759image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:28.164985image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:30.507766image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:32.498874image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:34.692095image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:36.811721image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:39.094459image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:41.214511image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:43.355445image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:45.565328image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:48.070349image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:49.978006image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:52.060866image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:19.613630image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:21.939893image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:24.042385image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:26.120502image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:28.400746image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:30.670761image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:32.655269image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:34.879167image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:37.098640image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:39.209226image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:41.351102image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:43.473707image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:45.676459image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:48.186903image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:50.089603image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:52.170910image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:19.860225image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:22.087820image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:24.151905image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:26.249297image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:28.526914image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:30.779446image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:32.796042image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:35.017331image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:37.312833image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:39.386544image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:41.466877image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:43.590677image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:45.823032image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:48.304664image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:50.198163image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:52.305484image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:19.970545image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:22.230533image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:24.291775image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:26.354520image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:28.627555image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:30.881495image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:32.911573image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:35.162676image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:37.453843image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:39.577082image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:41.588426image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:43.705423image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:45.946616image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:48.452018image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:50.306860image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:52.496765image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:20.115020image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:22.344955image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:24.396787image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:26.471446image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:28.742642image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:31.029232image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:33.044359image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:35.276206image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:37.566119image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:39.718309image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:41.747992image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:43.826950image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:46.063457image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:48.589225image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:50.428408image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:52.612287image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:20.245292image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:22.482080image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:24.509752image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:26.588028image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:28.978705image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:31.140821image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:33.175362image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:35.396862image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:37.677832image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:39.854777image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:41.904726image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:43.993209image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:46.251397image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:48.709790image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:50.540987image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:52.709808image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-05-20T00:01:24.642563image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-05-20T00:01:31.234655image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:33.313088image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:35.505965image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:37.807413image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:39.983559image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:42.008181image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:44.117219image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:46.382329image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:48.809896image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:50.666207image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:52.832351image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:20.472850image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:22.703396image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:24.780361image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:26.807922image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:29.223487image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-05-20T00:01:35.618520image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:37.916650image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:40.183250image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-05-20T00:01:52.978674image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:20.636079image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:22.820701image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:24.893203image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-05-20T00:01:29.351636image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:31.484343image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:33.527811image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:35.732283image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:38.027661image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:40.298187image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:42.275934image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:44.445353image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:46.727643image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:49.048027image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:50.894842image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:53.099938image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:20.754367image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:22.948576image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:25.023499image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:27.174548image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:29.484228image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:31.587820image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:33.638307image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-05-20T00:01:49.170205image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:51.011679image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:53.214534image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:20.878697image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:23.123894image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:25.150594image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:27.306660image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:29.620311image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:31.803646image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:33.752922image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:35.982565image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:38.284654image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:40.536927image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:42.531178image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:44.839643image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:47.222086image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:49.290348image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:51.126259image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:53.324126image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:21.000621image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:23.239724image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:25.274320image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:27.421441image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-05-20T00:01:31.915845image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-05-20T00:01:42.647766image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:44.951854image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:47.339659image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:49.407923image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:51.232880image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:53.430707image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:21.154883image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:23.361560image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:25.394910image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:27.542896image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:29.892598image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:32.060251image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:33.965972image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:36.286992image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:38.509086image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:40.767602image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:42.881990image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:45.079792image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:47.452232image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:49.523525image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:51.339435image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:53.544245image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:21.295368image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:23.487684image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:25.618855image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:27.667249image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:30.137880image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:32.174481image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:34.187633image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:36.484637image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:38.622946image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:40.883825image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:43.001706image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:45.196383image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:47.742837image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:49.643538image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:51.456008image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:53.646801image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:21.543142image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:23.601861image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:25.729620image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:27.778969image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:30.262962image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:32.283741image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:34.308612image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:36.594089image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:38.726632image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:40.993332image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:43.125265image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:45.341972image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:47.844421image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:49.750906image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:51.665083image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-05-20T00:02:30.088040image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
bodystyle_Cargo Vanbodystyle_Convertiblebodystyle_Coupebodystyle_Hatchbackbodystyle_Minivanbodystyle_Passenger Vanbodystyle_Pickup Truckbodystyle_SUVbodystyle_Sedanbodystyle_Wagonbodystyle_nancat_x0cat_x1cat_x2drivetrain_All-wheel Drivedrivetrain_Front-wheel Drivedrivetrain_Rear-wheel Driveexterior_color_x0exterior_color_x1exterior_color_x2exterior_color_x3exterior_color_x4fuel_type_Electricfuel_type_Flexiblefuel_type_Gasolinefuel_type_Hybridinterior_color_x0interior_color_x1interior_color_x2interior_color_x3interior_color_x4make_Acuramake_Audimake_BMWmake_Buickmake_Cadillacmake_Chevroletmake_Dodgemake_Fordmake_GMCmake_Hondamake_Hyundaimake_INFINITImake_Jeepmake_Kiamake_Land Rovermake_Lexusmake_Lincolnmake_Mazdamake_Mercedes-Benzmake_Nissanmake_Porschemake_RAMmake_Subarumake_Toyotamake_Volkswagenmake_Volvomileagemodel_hashed_0model_hashed_1model_hashed_10model_hashed_11model_hashed_12model_hashed_13model_hashed_14model_hashed_15model_hashed_16model_hashed_17model_hashed_18model_hashed_19model_hashed_2model_hashed_20model_hashed_21model_hashed_22model_hashed_23model_hashed_24model_hashed_25model_hashed_26model_hashed_27model_hashed_28model_hashed_29model_hashed_3model_hashed_30model_hashed_31model_hashed_32model_hashed_33model_hashed_34model_hashed_35model_hashed_36model_hashed_37model_hashed_38model_hashed_39model_hashed_4model_hashed_40model_hashed_41model_hashed_42model_hashed_43model_hashed_44model_hashed_45model_hashed_46model_hashed_47model_hashed_48model_hashed_49model_hashed_5model_hashed_50model_hashed_51model_hashed_6model_hashed_7model_hashed_8model_hashed_9msrpstock_typeyear
bodystyle_Cargo Van1.0000.0050.0150.0070.0000.0000.0300.1470.0440.0000.0000.137-0.1710.1190.1360.0050.2730.025-0.019-0.0190.045-0.0520.0180.0000.0280.018-0.009-0.0250.0190.051-0.0500.0120.0170.0220.0090.0170.0210.0160.0470.0130.0180.0250.0100.0280.0160.0090.0130.0120.0170.2180.0120.0020.0670.0200.0180.0240.007-0.0210.0160.0270.0050.0050.0310.0120.0080.0580.0120.0280.0120.0140.0090.0190.0090.0150.0670.0100.0760.0070.0190.0140.0190.0240.0140.0060.0120.0130.0110.0060.0000.0150.0100.0280.0050.0150.0020.0050.0090.0000.0100.0100.0060.0220.0060.0100.0100.0150.0060.4440.0000.0140.0780.0260.023
bodystyle_Convertible0.0051.0000.0120.0050.0000.0000.0250.1250.0370.0000.000-0.0070.026-0.0330.0460.0310.162-0.016-0.0040.0090.0040.0110.0140.0000.0230.015-0.017-0.0110.0090.020-0.0220.0100.0430.1370.0110.0140.0140.0130.0000.0110.0140.0210.0080.0190.0130.0060.0100.0090.0300.0350.0190.0000.0080.0170.0130.0160.0000.0400.0130.0060.0000.1290.0160.0160.0170.0000.0150.0490.0090.0150.0000.0120.0060.0090.0110.0080.0070.0160.0060.0110.0140.0070.0390.0040.0100.0100.0080.0030.0790.0080.0080.0620.0170.0120.0000.0030.0070.1350.0000.0210.0350.0150.0000.0080.0140.0640.0220.0130.1090.0110.0430.054-0.056
bodystyle_Coupe0.0150.0121.0000.0160.0000.0080.0510.2490.0750.0070.005-0.0650.160-0.1720.1690.0720.500-0.003-0.001-0.0210.0290.0070.0310.0070.0440.027-0.025-0.0060.0270.040-0.0460.0200.0440.0960.0140.0220.0190.1700.0150.0250.0220.0350.0000.0490.0290.0180.0190.0220.0270.0120.0160.0530.0200.0340.0060.0400.0150.0870.0550.0160.0120.0550.0330.0280.0220.0610.0550.1570.0230.0240.0100.0250.0100.0100.0180.0560.0190.0070.0100.0210.0220.0080.0180.0070.0000.0250.0190.0410.0280.1090.0210.0150.0170.0270.0100.0120.0060.0610.0160.0030.1260.0330.0450.0180.0070.0090.0000.2170.0890.0190.1020.114-0.148
bodystyle_Hatchback0.0070.0050.0161.0000.0000.0000.0300.1490.0450.0000.0000.084-0.116-0.0080.0210.0330.0190.012-0.0060.006-0.0240.0170.0180.0000.0260.015-0.017-0.0110.0280.041-0.0490.0700.1340.0130.0090.0170.0220.0160.0230.0140.0150.0260.0100.0290.0150.0090.0130.0120.0160.0000.0200.0020.0110.0740.0150.0220.0070.0060.0470.0310.0100.0050.0190.0140.0170.0150.0120.0090.0120.0040.0380.0190.0090.0110.0190.0070.0040.0030.0200.0110.0190.0130.0150.2510.0130.0130.0110.0060.0000.0080.0300.0250.0490.0150.0020.0920.0100.0000.0070.0710.0060.0220.0040.0000.0100.0880.0070.0090.1570.014-0.0040.004-0.004
bodystyle_Minivan0.0000.0000.0000.0001.0000.0000.0060.0440.0110.0000.0000.041-0.0530.0410.0470.0540.0000.005-0.0060.012-0.0130.0090.0000.0000.0000.000-0.0150.0080.008-0.0150.0140.0000.0000.0000.0070.0000.0070.0000.0000.0000.0820.0040.0000.0050.0370.0000.0000.0000.0000.0040.0040.0000.0000.0000.0030.0030.000-0.0260.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1390.0000.0000.0000.0000.0000.0000.0000.0000.0000.0150.0000.0140.0000.0000.0120.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0550.0000.0000.0000.0000.0000.0000.0000.000-0.0020.0190.016
bodystyle_Passenger Van0.0000.0000.0080.0000.0001.0000.0180.0930.0270.0000.0000.086-0.1090.0810.1090.0560.1190.0010.0120.011-0.0100.0160.0100.0000.0150.0070.006-0.025-0.0050.028-0.0220.0060.0090.0130.0010.0090.0200.0250.0170.0070.0940.0150.0040.0170.0230.0010.0060.0050.0000.0960.0150.0000.0040.0120.0040.0140.000-0.0070.0080.2640.0030.0000.0110.0110.0080.0000.0050.0060.0050.0090.0000.0110.0000.0350.1660.0030.0020.0000.0110.0070.0100.0090.0070.0000.0000.0060.0040.0000.0000.0080.0020.0030.0000.0070.0000.0000.0020.0000.0030.0030.0000.0220.0000.0030.0030.0080.0000.1040.0030.0070.0140.008-0.007
bodystyle_Pickup Truck0.0300.0250.0510.0300.0060.0181.0000.4470.1350.0160.0140.306-0.3830.0990.1420.1430.018-0.0020.048-0.0480.069-0.0420.0510.0000.0550.0280.0740.035-0.0420.066-0.0650.0430.0560.0690.0330.0550.1920.0460.1200.2150.0410.0320.0370.0320.0540.0330.0430.0420.0560.0770.0210.0210.3830.0650.0250.0760.028-0.0140.3930.0310.0700.0240.0610.1050.0440.0250.1250.0390.0420.0790.0340.0490.0340.0490.0480.0320.0390.0300.0560.1930.0600.0430.2710.0160.3420.0440.0260.0270.0200.0460.0360.0190.0260.0490.0210.0270.0350.0300.0320.1650.0280.0660.1230.0560.0360.0410.0280.0310.0340.0470.1700.0170.023
bodystyle_SUV0.1470.1250.2490.1490.0440.0930.4471.0000.6530.0860.078-0.4330.5770.0010.3230.2120.2660.004-0.015-0.017-0.043-0.0200.0960.0160.1170.065-0.0660.0560.062-0.0960.0850.0110.0630.0880.0510.0580.0700.0090.0110.0530.0070.0340.0310.1640.0440.0740.0140.0740.0830.1120.0700.0230.1870.0700.0390.0260.002-0.1430.1940.0820.0560.0420.1230.0990.0520.0750.1910.0970.0800.1390.1220.1140.0270.0860.1210.0830.0500.0650.1030.1410.1170.0800.1540.0440.1510.0820.1300.0450.0550.0850.0960.0000.1000.0840.0260.0290.0760.0540.1440.1140.0220.1400.0670.0110.0800.0580.0130.1860.0540.0860.0130.1630.186
bodystyle_Sedan0.0440.0370.0750.0450.0110.0270.1350.6531.0000.0250.0220.256-0.398-0.0540.3590.3840.000-0.010-0.0110.060-0.0130.0530.0470.0110.0560.0290.046-0.079-0.0680.020-0.0070.0130.0520.0970.0270.0020.0390.0180.1140.0670.0360.0240.0000.1300.0040.0490.0580.0350.0530.1090.1320.0300.0560.0300.0310.0390.0320.1380.0690.0510.0360.0330.0830.0800.0500.0940.1590.0450.0440.1390.1670.0780.0300.0680.1020.0890.0520.0570.0660.0490.0730.1280.0460.0000.0590.0520.1750.0530.0620.0730.1380.0070.1180.0750.0090.0170.0520.0130.1960.0890.0420.0990.0280.0490.0870.0560.0320.1360.0460.093-0.2160.154-0.163
bodystyle_Wagon0.0000.0000.0070.0000.0000.0000.0160.0860.0251.0000.0000.062-0.0770.0770.0120.0200.010-0.0100.0050.031-0.0030.0240.0090.0000.0150.0090.0120.002-0.0220.010-0.0030.0050.0680.0090.0000.0080.0180.0070.0110.0050.0090.0140.0020.0160.0080.0000.0050.0040.0080.0010.0000.0000.0030.0000.0000.0740.0760.0600.0090.0270.0430.0670.0100.0100.0070.0280.0030.0000.0040.0060.0000.0100.0000.0160.0030.0010.0000.0000.0100.0050.0090.0150.0340.0000.2230.0050.0020.0460.0000.0070.0440.0000.0500.0070.0000.0000.0000.0000.0000.0000.0000.0030.0000.0010.0170.0060.0050.0190.0000.006-0.0260.055-0.076
bodystyle_nan0.0000.0000.0050.0000.0000.0000.0140.0780.0220.0001.0000.005-0.0180.0150.0000.0000.0000.012-0.007-0.002-0.000-0.0020.0070.0000.0130.0070.0070.003-0.012-0.0170.0280.0180.0000.0100.0000.0140.0160.0000.0040.0040.0030.0120.0000.0140.0060.0010.0030.0020.0070.0000.0010.0000.0220.0060.0030.0770.0000.0130.0000.0000.0360.0000.0040.0050.0070.0090.0000.0000.0000.0040.0000.0080.0000.0000.0470.0000.0000.0000.0000.0040.0080.0030.0000.0000.0130.0450.0000.0000.0000.0050.0160.0000.0100.0020.0470.0000.0000.0000.0000.0000.0000.0540.0000.0000.0000.0000.0000.0000.0000.004-0.0090.000-0.013
cat_x00.137-0.007-0.0650.0840.0410.0860.306-0.4330.2560.0620.0051.000-0.620-0.1600.3970.3730.537-0.0020.050-0.0120.028-0.0060.3380.1040.4000.4800.058-0.094-0.0660.053-0.0430.1740.2330.3080.1480.1630.1640.4590.2070.2100.1510.2030.1410.4540.1160.1780.1570.1640.2110.4020.2430.0850.4000.1420.1330.2240.1080.0150.3040.1320.1630.0790.3340.1890.1310.1190.1160.1850.1350.1090.2250.1920.1290.1680.1630.1770.1280.1050.2010.1240.1450.0950.2140.0760.2740.1210.1020.1490.0880.1740.1060.0980.2630.0920.0940.1200.1180.1060.0940.1490.1210.1650.1210.1340.1210.1610.1030.3780.1510.1160.1460.217-0.027
cat_x1-0.1710.0260.160-0.116-0.053-0.109-0.3830.577-0.398-0.077-0.018-0.6201.0000.0120.3500.3950.338-0.033-0.001-0.0310.015-0.0390.1070.1770.1780.1410.0030.050-0.022-0.0780.0930.1220.2180.1990.0980.1470.1630.1230.0980.1850.2240.1940.1450.1390.1330.1170.1610.1290.1990.3200.2390.1320.3180.1140.0550.2560.1070.0050.2380.0740.0880.1410.0910.1190.1120.1570.0990.0630.0950.1940.2280.1400.0590.1200.2080.1410.0770.0780.1140.1390.1210.0770.1870.0530.2820.1010.1020.0730.0950.1780.1100.0480.1290.0440.0640.0420.1110.1450.0790.1410.0840.1620.1140.1110.1150.1060.0790.1530.0790.0640.0280.1740.003
cat_x20.119-0.033-0.172-0.0080.0410.0810.0990.001-0.0540.0770.015-0.1600.0121.0000.4340.4350.580-0.019-0.0070.0060.0860.0110.1210.0830.2940.449-0.0050.035-0.0270.049-0.0130.1010.1680.2280.0850.1160.2750.3120.1680.2680.0920.1120.0880.2010.0700.0990.1870.1060.0980.1810.1960.0730.2680.1570.0820.2080.1430.0630.1800.0490.1750.0960.2690.1780.0800.1150.0530.1620.1580.1450.2070.1030.0590.1920.1260.1290.1300.0800.1310.1210.1170.1950.1430.1050.2640.0890.2380.1110.0790.1220.0950.0600.1810.0990.0670.1470.0760.0680.2500.3060.1370.1550.1170.0970.1250.0710.0930.2770.1020.0910.2750.170-0.033
drivetrain_All-wheel Drive0.1360.0460.1690.0210.0470.1090.1420.3230.3590.0120.0000.3970.3500.4341.0000.8830.3760.0100.053-0.0080.0320.0080.0600.0110.0790.048-0.083-0.0270.0290.056-0.0320.0000.0950.0720.0800.0330.2180.0410.0180.0210.0550.0760.0560.1340.0520.0620.0170.0420.0550.0640.1500.0210.0450.1190.0460.0110.036-0.1030.0780.0920.0460.0340.0780.1490.0440.0850.0480.0390.0580.0990.0450.0890.0170.0730.0750.0000.0280.0150.0590.0270.0310.1210.0570.0690.0580.0650.1390.0360.0150.1390.0760.0310.0310.0460.0310.0290.0590.0190.1560.1110.0610.0920.0270.1920.0310.0590.0310.1960.0340.0650.4840.1230.164
drivetrain_Front-wheel Drive0.0050.0310.0720.0330.0540.0560.1430.2120.3840.0200.0000.3730.3950.4350.8831.0000.103-0.015-0.0560.020-0.057-0.0010.0680.0060.0720.0310.0890.037-0.039-0.0980.0710.0100.0840.1090.0960.0480.2180.0550.0390.0100.0790.1040.0540.1160.0730.0550.0130.0380.0510.1220.1780.0350.0360.1050.0500.0350.0290.0740.0760.0440.0420.0260.0780.1520.0600.0970.0540.0560.0600.1190.0430.0790.0170.0710.0840.0130.0260.0120.0460.0210.0500.1400.0650.0780.0610.0540.1570.0350.0330.1060.0860.0280.0330.0540.0250.0290.0560.0270.1630.1280.0040.1110.0240.2170.0350.0600.0260.1340.0270.057-0.5650.090-0.108
drivetrain_Rear-wheel Drive0.2730.1620.5000.0190.0000.1190.0180.2660.0000.0100.0000.5370.3380.5800.3760.1031.0000.010-0.001-0.0220.045-0.0150.0040.0080.0260.040-0.000-0.0160.0150.073-0.0730.0290.0340.0600.0200.0230.0300.1970.0370.0220.0390.0430.0110.0540.0330.0220.0090.0130.0150.1060.0320.0230.0240.0430.0000.0430.0190.0720.0200.1090.0170.0260.0100.0230.1310.0100.0000.1490.0170.0250.0130.0320.0070.0160.0270.0190.0190.0100.0370.0260.0330.0250.0080.0070.0000.0290.0260.0200.0610.0860.0040.0090.0140.0340.0140.0080.0130.0650.0150.0170.1150.0390.0160.0210.0120.0270.0140.2710.0510.0290.0880.084-0.133
exterior_color_x00.025-0.016-0.0030.0120.0050.001-0.0020.004-0.010-0.0100.012-0.002-0.033-0.0190.010-0.0150.0101.0000.275-0.5580.227-0.2560.1000.0410.0770.055-0.021-0.038-0.0120.021-0.0030.0690.0560.1330.0900.3290.2130.1590.1210.0900.1020.1330.0540.2640.1770.0730.0910.0590.1300.1010.1080.0540.1770.1490.0680.1990.041-0.0310.0500.0450.0300.0230.1200.0700.0420.0390.0480.0430.0620.0440.0480.0980.0470.0690.0360.0710.0410.0210.1040.0590.0620.0470.0810.0310.0650.0720.0400.0400.0360.0530.0390.0250.0300.0590.1200.0310.0770.0380.0260.0500.0230.0860.0600.0260.0370.1250.0210.0610.1210.0610.0050.1180.033
exterior_color_x1-0.019-0.004-0.001-0.006-0.0060.0120.048-0.015-0.0110.005-0.0070.050-0.001-0.0070.053-0.056-0.0010.2751.000-0.110-0.0580.0980.0590.0430.0580.0520.017-0.049-0.0300.110-0.1050.0970.0870.1380.0890.0830.1350.0600.1230.0820.1630.1060.0880.2980.1870.0880.1130.0680.1490.1430.1220.0390.1480.1740.1850.3030.0350.0090.0350.0480.0350.0210.1040.0530.0490.0400.0420.0940.0300.0420.0580.1110.0230.0920.0430.0380.0460.0290.0790.0380.0520.0400.0830.0350.0360.1060.0650.0170.0290.0760.0410.0360.0380.0550.0330.0350.0880.0780.0560.0450.0260.1170.0520.0320.0380.0480.0100.0660.0300.0500.0630.102-0.002
exterior_color_x2-0.0190.009-0.0210.0060.0120.011-0.048-0.0170.0600.031-0.002-0.012-0.0310.006-0.0080.020-0.022-0.558-0.1101.000-0.2340.5100.0700.0220.0680.054-0.0060.0430.020-0.006-0.0070.0850.0470.1010.0820.1090.1700.1770.1220.0700.1440.0650.0740.3060.1340.0880.0800.1400.1700.1700.1150.0380.1700.1220.1540.1630.0280.0250.0340.0620.0450.0160.1350.0630.0510.0500.0460.0570.0380.0400.0620.1060.0410.0710.0490.0390.0210.0410.0760.0240.0550.0470.0890.0400.0570.0650.0620.0240.0480.0570.0310.0340.0290.0450.0440.0290.0900.1180.0480.0370.0240.0630.0620.0530.0410.0560.0330.0470.0600.065-0.0530.119-0.023
exterior_color_x30.0450.0040.029-0.024-0.013-0.0100.069-0.043-0.013-0.003-0.0000.0280.0150.0860.032-0.0570.0450.227-0.058-0.2341.000-0.3070.0680.0120.0450.0210.013-0.028-0.0190.045-0.0270.0670.0620.0850.2240.0920.0940.1280.0800.1390.1050.1210.0750.4860.1830.0380.0610.0440.1570.1510.0680.0430.1890.1090.0890.1370.0300.0320.0430.0310.0670.0220.1440.0490.0410.0410.0220.0480.0360.0650.0550.1230.0160.0590.0500.0540.0230.0520.0690.0540.0870.0420.0820.0170.0490.0520.0450.0200.0530.0920.0380.0340.0360.0700.0310.0330.0910.1200.0350.0340.0150.0490.0560.0290.0970.0480.0200.0540.0220.0390.1130.096-0.034
exterior_color_x4-0.0520.0110.0070.0170.0090.016-0.042-0.0200.0530.024-0.002-0.006-0.0390.0110.008-0.001-0.015-0.2560.0980.510-0.3071.0000.0660.0310.0450.0430.043-0.046-0.037-0.0030.0240.1050.0770.0730.0980.3320.1370.1460.1350.1120.1110.0550.0910.2690.0950.0580.1900.0650.1270.1350.1530.0520.1430.2100.1360.1180.0430.0140.0430.0380.0370.0180.1200.0440.0620.0490.0640.0400.0400.0470.0550.0920.0530.1100.0390.0350.0280.0290.0640.0690.0650.0480.0630.0300.0450.0450.0520.0270.0180.0530.0310.0310.0260.0350.1630.0310.0560.0260.0450.0380.0250.0510.0570.0510.0540.0710.0340.0610.1230.068-0.0120.119-0.020
fuel_type_Electric0.0180.0140.0310.0180.0000.0100.0510.0960.0470.0090.0070.3380.1070.1210.0600.0680.0040.1000.0590.0700.0680.0661.0000.0090.6700.036-0.015-0.0230.0230.035-0.0300.0260.0160.0270.0200.1220.0160.0320.0460.0000.0360.0000.0230.0550.0720.0200.0240.0260.0350.0530.0000.0070.0230.0360.0270.0310.000-0.1080.0320.0590.0220.0230.0380.0320.0310.0150.0250.0250.0270.0250.0210.0380.1430.0300.1970.1140.0180.0180.0370.0210.0330.1410.0180.0190.0260.0150.0230.0560.0110.0260.0190.0110.0160.0290.0120.0100.0210.2210.0150.0220.0170.0890.0170.0230.0180.2180.0170.2460.0220.0290.1980.1190.088
fuel_type_Flexible0.0000.0000.0070.0000.0000.0000.0000.0160.0110.0000.0000.1040.1770.0830.0110.0060.0080.0410.0430.0220.0120.0310.0091.0000.2110.009-0.0130.0030.0220.011-0.0140.0050.0000.0120.0000.0150.0000.0080.0800.0060.0090.0140.0030.0030.0080.0000.0050.0000.0090.0000.0140.0000.0030.0110.0090.0130.0000.0820.0190.0000.0140.0000.0280.0140.0000.0000.0000.0050.0050.0060.0070.0070.0000.0390.0060.0300.0000.0000.0610.0230.0040.0050.0060.0000.0050.0050.0030.0000.0000.0150.0000.0020.0000.0070.0000.0000.0000.0000.0010.0000.0000.0040.0000.0020.0170.0070.0000.0080.0000.006-0.0240.066-0.094
fuel_type_Gasoline0.0280.0230.0440.0260.0000.0150.0550.1170.0560.0150.0130.4000.1780.2940.0790.0720.0260.0770.0580.0680.0450.0450.6700.2111.0000.6810.0250.038-0.028-0.0640.0580.0340.0270.0070.0290.0640.0320.0000.0580.0220.0000.0190.0330.0420.0600.0310.0100.0000.0130.0140.0370.0000.0350.0570.0880.0590.0540.1080.0260.0680.0470.0450.0480.0490.0410.0190.0350.0780.0330.0400.0170.0430.0930.0450.1240.0730.0350.0390.0290.0160.0160.0880.0270.0290.1290.0680.0250.0300.0120.0270.0220.0210.0050.0250.0200.0090.0660.1900.0290.0240.0260.0570.0220.0300.0260.1350.0670.1540.0320.089-0.2030.116-0.090
fuel_type_Hybrid0.0180.0150.0270.0150.0000.0070.0280.0650.0290.0090.0070.4800.1410.4490.0480.0310.0400.0550.0520.0540.0210.0430.0360.0090.6811.000-0.016-0.0320.0100.052-0.0480.0190.0200.0090.0200.0340.0630.0300.0090.0290.0420.0290.0210.0050.0150.0200.0440.0250.0590.0310.0510.0070.0240.0400.1560.0480.083-0.0720.0110.0410.0870.0630.0390.0380.0250.0210.0300.1210.0210.0300.0140.0230.0080.0390.0260.0180.0420.0530.0370.0210.0530.0280.0200.0290.1900.0990.0070.0160.0270.0230.0310.0160.0000.0600.0120.0160.0910.0510.0220.0240.0170.0320.0110.0190.0330.0260.1020.0220.0220.1480.0980.0670.071
interior_color_x0-0.009-0.017-0.025-0.017-0.0150.0060.074-0.0660.0460.0120.0070.0580.003-0.005-0.0830.089-0.000-0.0210.017-0.0060.0130.043-0.015-0.0130.025-0.0161.000-0.657-0.8050.264-0.0790.1130.0930.0810.0500.1510.4260.0920.1490.2460.0930.1830.1700.1640.0860.0400.0540.1020.1000.0990.0910.0330.0870.1290.0780.1020.063-0.0550.0450.0350.0470.0250.0630.1360.0590.0280.0420.0400.0400.0950.0470.0630.0250.0880.0500.0300.0490.0350.0860.0690.0590.0280.2680.0520.0950.0310.0570.0940.0480.0860.0440.0250.0430.0770.0680.0320.0420.0240.0290.0260.0590.1160.0350.0510.1200.0460.0370.0550.0770.0690.0040.0780.043
interior_color_x1-0.025-0.011-0.006-0.0110.008-0.0250.0350.056-0.0790.0020.003-0.0940.0500.035-0.0270.037-0.016-0.038-0.0490.043-0.028-0.046-0.0230.0030.038-0.032-0.6571.0000.612-0.4490.2560.1950.1240.1380.2220.1580.4500.1570.3000.2510.1150.1800.1690.2080.1300.1490.0860.1250.0860.1530.4990.0480.0980.1190.1420.1420.2040.0550.0740.0550.0660.0360.1000.1860.1610.0440.1040.0660.1330.0940.0440.1250.0960.0620.0470.0480.0550.0530.1190.0590.1260.1230.2730.0640.0900.0770.0520.0530.0330.1110.0490.0300.0340.0820.0570.0510.0470.0410.0710.0370.0690.1260.0420.1300.1190.0830.0420.0790.0480.068-0.0950.111-0.064
interior_color_x20.0190.0090.0270.0280.008-0.005-0.0420.062-0.068-0.022-0.012-0.066-0.022-0.0270.029-0.0390.015-0.012-0.0300.020-0.019-0.0370.0230.022-0.0280.010-0.8050.6121.000-0.2330.0070.2700.1310.1480.2630.1060.3640.1590.4040.2010.1330.2880.2420.3320.1350.2290.1240.1190.1010.1910.3550.0470.1300.2010.0930.0960.1290.0470.0480.0680.0530.0470.1610.1500.1420.0620.0690.0600.1870.0940.0980.1920.0880.0980.0720.0500.0680.0440.1630.0560.1910.1050.1340.0710.0850.0510.0670.0470.0340.0860.1760.0690.0670.0850.0460.0520.0560.0360.0570.0640.0500.1460.0450.1030.1030.1010.0390.0610.0480.099-0.0590.103-0.041
interior_color_x30.0510.0200.0400.041-0.0150.0280.066-0.0960.0200.010-0.0170.053-0.0780.0490.056-0.0980.0730.0210.110-0.0060.045-0.0030.0350.011-0.0640.0520.264-0.449-0.2331.000-0.8940.2790.1310.1500.2050.0910.3210.1620.4530.1810.1330.2910.2720.1840.1510.2030.0740.1560.1160.1150.5020.0450.1410.2030.0390.1050.1520.0280.0760.0740.0410.0420.0940.1500.2110.0570.1020.0690.1750.1040.0630.1380.1070.0930.0750.0550.0770.0430.1700.0570.1910.1210.2110.0680.0820.0410.1150.0370.0360.0740.0440.0340.0520.0880.0480.0270.0580.0460.0620.0480.0420.1270.0450.1510.0940.0890.0490.0740.0390.0960.0800.0560.012
interior_color_x4-0.050-0.022-0.046-0.0490.014-0.022-0.0650.085-0.007-0.0030.028-0.0430.093-0.013-0.0320.071-0.073-0.003-0.105-0.007-0.0270.024-0.030-0.0140.058-0.048-0.0790.2560.007-0.8941.0000.1980.1260.1460.1870.1020.4090.1750.2850.2230.1230.1690.1650.2140.1480.1460.0470.1560.1250.1380.4970.0480.1120.1520.0760.1830.131-0.0220.0700.0750.0370.0270.1050.1510.1890.0520.1260.0670.1030.0910.0760.1450.1050.1050.0560.0410.0500.0410.1190.0630.1170.1210.1100.0540.0950.0530.0540.0470.0480.0870.0670.0370.0360.0830.0470.0400.1000.0450.0430.0560.0560.1510.0420.1330.1120.0530.0280.0790.0370.055-0.0290.087-0.016
make_Acura0.0120.0100.0200.0700.0000.0060.0430.0110.0130.0050.0180.1740.1220.1010.0000.0100.0290.0690.0970.0850.0670.1050.0260.0050.0340.0190.1130.1950.2700.2790.1981.0000.0260.0320.0140.0250.0470.0240.0450.0210.0260.0370.0170.0420.0250.0140.0190.0190.0260.0370.0380.0080.0170.0300.0260.0350.0120.0450.0240.0130.0160.0080.0280.0290.0230.0100.0180.0200.0190.0220.0150.0280.0070.0220.3950.2300.0150.0130.5960.0210.0280.0200.0220.2470.0190.0200.0120.0110.0070.0230.0120.0160.0110.0220.0080.0110.0150.0070.0160.0160.0120.0320.0120.0160.0160.0230.0550.0250.0160.021-0.0250.048-0.032
make_Audi0.0170.0430.0440.1340.0000.0090.0560.0630.0520.0680.0000.2330.2180.1680.0950.0840.0340.0560.0870.0470.0620.0770.0160.0000.0270.0200.0930.1240.1310.1310.1260.0261.0000.0420.0190.0330.0610.0310.0590.0270.0350.0480.0220.0540.0320.0200.0260.0250.0340.0490.0490.0110.0220.0390.0340.0460.0160.0750.0700.0180.0210.1630.0370.0290.1670.0140.0150.0160.0250.0280.0460.0370.0200.3760.0190.0220.0180.0180.0380.0090.0360.0210.0210.0210.0260.0140.0470.0110.2740.0310.0160.0440.0250.0140.0120.0150.0950.0100.0210.0220.0160.0340.0670.0220.1660.0940.0160.0450.0210.0280.0980.079-0.074
make_BMW0.0220.1370.0960.0130.0000.0130.0690.0880.0970.0090.0100.3080.1990.2280.0720.1090.0600.1330.1380.1010.0850.0730.0270.0120.0070.0090.0810.1380.1480.1500.1460.0320.0421.0000.0240.0410.0750.0380.0720.0340.0430.0590.0280.0670.0400.0250.0320.0310.0420.0600.0600.0150.0280.0480.0430.0560.0210.0890.0290.0150.0270.0370.0460.0440.0320.0270.0260.2220.0310.0510.0140.0460.0250.0150.0280.0270.0240.0220.0150.0210.1850.0260.0120.0600.0450.0120.0230.1190.0230.0250.1070.0850.0680.0230.0080.0180.0260.1130.0210.0200.0200.0140.0120.0270.0290.0320.0160.0400.1260.0270.1250.104-0.050
make_Buick0.0090.0110.0140.0090.0070.0010.0330.0510.0270.0000.0000.1480.0980.0850.0800.0960.0200.0900.0890.0820.2240.0980.0200.0000.0290.0200.0500.2220.2630.2050.1870.0140.0190.0241.0000.0190.0360.0180.0350.0150.0200.0280.0120.0320.0190.0100.0140.0140.0190.0290.0290.0040.0120.0230.0200.0270.0080.0240.0160.0090.0110.2020.0220.1250.0170.0060.0130.0150.1560.0440.0100.0210.0110.0170.0160.0100.0110.0090.0220.0160.4030.0150.0160.0080.0140.0150.0120.0070.0020.0150.0110.0120.0070.0170.0040.0070.0110.0020.0120.0130.0080.1160.0080.0120.0110.0170.0080.0190.0120.016-0.0850.036-0.049
make_Cadillac0.0170.0140.0220.0170.0000.0090.0550.0580.0020.0080.0140.1630.1470.1160.0330.0480.0230.3290.0830.1090.0920.3320.1220.0150.0640.0340.1510.1580.1060.0910.1020.0250.0330.0410.0191.0000.0600.0300.0570.0270.0340.0470.0220.0530.0320.0190.0250.0240.0330.0470.0480.0110.0220.0380.0340.0450.0160.0010.0310.0170.0210.0310.0360.0370.0300.0140.0240.0250.0240.0280.0160.0360.0660.0270.0200.0210.0200.0160.0370.1950.0350.0260.0230.0160.0240.0250.0220.0150.1170.0240.0330.0270.0100.0280.3250.0150.0200.0100.0210.0170.0160.0410.0070.0190.1390.2310.0160.0310.4080.0270.0820.0040.002
make_Chevrolet0.0210.0140.0190.0220.0070.0200.1920.0700.0390.0180.0160.1640.1630.2750.2180.2180.0300.2130.1350.1700.0940.1370.0160.0000.0320.0630.4260.4500.3640.3210.4090.0470.0610.0750.0360.0601.0000.0560.1050.0500.0630.0870.0410.0970.0590.0360.0470.0460.0610.0870.0880.0230.0420.0710.0620.0820.0310.0170.0520.0200.0400.0280.0670.3800.0540.0270.0440.0390.0660.1960.0370.0650.0370.0500.1000.0280.0390.0330.0620.0890.0660.0480.0520.0310.3130.0480.0290.0290.0150.3020.0390.0240.0290.1360.0230.0750.0380.0210.0350.0390.2160.3050.0300.0400.0390.0540.0310.0580.0400.051-0.1480.060-0.049
make_Dodge0.0160.0130.1700.0160.0000.0250.0460.0090.0180.0070.0000.4590.1230.3120.0410.0550.1970.1590.0600.1770.1280.1460.0320.0080.0000.0300.0920.1570.1590.1620.1750.0240.0310.0380.0180.0300.0561.0000.0540.0250.0320.0440.0200.0500.0300.0180.0240.0230.0310.0450.0450.0100.0210.0360.0320.0420.0150.0070.0280.0160.0170.0130.5510.0300.1530.0130.0220.0240.0230.0260.0180.0250.0180.0130.0260.0110.0190.0160.2790.0250.0130.0240.0270.0150.0230.0240.0210.0140.0090.0280.0150.0200.0140.0270.0100.0140.0190.0090.0200.0200.0150.0340.0150.0200.0190.0270.0150.2140.0200.0260.0480.033-0.004
make_Ford0.0470.0000.0150.0230.0000.0170.1200.0110.1140.0110.0040.2070.0980.1680.0180.0390.0370.1210.1230.1220.0800.1350.0460.0800.0580.0090.1490.3000.4040.4530.2850.0450.0590.0720.0350.0570.1050.0541.0000.0480.0600.0830.0390.0930.0560.0350.0450.0440.0590.0840.0850.0220.0400.0680.0600.0790.030-0.0120.2000.0290.0380.0260.0500.1710.0440.0260.0260.0980.2880.0450.0360.0610.0350.0510.0330.0520.0370.0300.0480.0440.3580.0410.3090.0300.0330.0460.0390.0280.0210.0530.0300.0380.0210.0240.0000.0280.0280.0200.0380.0390.0290.0550.0220.0550.0380.0520.0300.1330.0380.0460.0110.028-0.022
make_GMC0.0130.0110.0250.0140.0000.0070.2150.0530.0670.0050.0040.2100.1850.2680.0210.0100.0220.0900.0820.0700.1390.1120.0000.0060.0220.0290.2460.2510.2010.1810.2230.0210.0270.0340.0150.0270.0500.0250.0481.0000.0280.0390.0180.0440.0260.0160.0210.0200.0270.0400.0400.0090.0180.0320.0280.0370.0130.0310.3170.0140.0170.0110.0300.0310.0250.0110.0190.0210.0200.0230.0160.0300.0820.0240.0230.0180.0170.0140.0310.1440.0300.0140.0240.0540.0210.0440.0180.0420.0080.0250.0170.0170.0120.0240.0090.0120.0170.0630.0170.0990.0130.0340.0130.0180.4280.0180.0130.0230.0170.0230.0140.044-0.036
make_Honda0.0180.0140.0220.0150.0820.0940.0410.0070.0360.0090.0030.1510.2240.0920.0550.0790.0390.1020.1630.1440.1050.1110.0360.0090.0000.0420.0930.1150.1330.1330.1230.0260.0350.0430.0200.0340.0630.0320.0600.0281.0000.0500.0230.0550.0330.0200.0270.0260.0350.0500.0500.0120.0230.0400.0350.0470.0170.0500.0320.0190.0220.0150.0380.0370.0310.0150.0120.0270.0260.0290.0170.0380.0190.0300.0640.0230.0210.0180.0390.1210.0340.0270.0300.0170.0260.0270.3730.0160.0110.3790.0220.0220.0160.0490.0120.0160.0210.0520.0220.2920.1170.0420.3020.0230.0220.0310.0170.0330.0220.029-0.0990.072-0.057
make_Hyundai0.0250.0210.0350.0260.0040.0150.0320.0340.0240.0140.0120.2030.1940.1120.0760.1040.0430.1330.1060.0650.1210.0550.0000.0140.0190.0290.1830.1800.2880.2910.1690.0370.0480.0590.0280.0470.0870.0440.0830.0390.0501.0000.0320.0770.0460.0290.0370.0360.0480.0690.0700.0180.0330.0560.0490.0650.024-0.0790.0450.0260.0310.0390.1610.0530.0430.0160.0350.0350.0700.0410.0290.0520.0290.0420.0390.0320.2920.0420.3780.0400.0520.1180.0420.0240.1380.0370.3000.0160.0170.0410.1940.0320.0230.0420.0180.0230.0300.0080.0310.1260.0240.0350.0240.0280.0310.0430.0180.0460.0310.168-0.1440.1120.103
make_INFINITI0.0100.0080.0000.0100.0000.0040.0370.0310.0000.0020.0000.1410.1450.0880.0560.0540.0110.0540.0880.0740.0750.0910.0230.0030.0330.0210.1700.1690.2420.2720.1650.0170.0220.0280.0120.0220.0410.0200.0390.0180.0230.0321.0000.0360.0210.0120.0170.0160.0220.0320.0330.0060.0140.0260.0230.0300.0100.0170.0210.0110.0130.0050.0240.0250.0200.0190.0770.0120.0160.0180.0120.0240.0120.0190.0790.0120.0130.0110.0250.0180.0230.0700.0190.0100.0400.0170.0140.0090.0050.0190.0130.0180.2120.3310.0060.0240.0870.0040.0130.0520.0090.0280.0070.0100.0070.1580.0100.0210.0130.0180.0290.012-0.024
make_Jeep0.0280.0190.0490.0290.0050.0170.0320.1640.1300.0160.0140.4540.1390.2010.1340.1160.0540.2640.2980.3060.4860.2690.0550.0030.0420.0050.1640.2080.3320.1840.2140.0420.0540.0670.0320.0530.0970.0500.0930.0440.0550.0770.0361.0000.0520.0320.0420.0400.0540.0770.0780.0200.0370.0620.0550.0730.027-0.0000.0500.0300.0860.0250.3670.0600.0490.1680.0390.0380.0410.0830.0330.4470.0210.0460.0790.0360.0290.0910.0600.0450.0580.0430.0470.0270.0410.0420.0370.0260.0190.1540.0350.0360.0250.0480.0200.1030.0340.0190.0510.0300.0090.0400.0280.0360.0350.0480.0270.0520.0350.0420.1350.0520.031
make_Kia0.0160.0130.0290.0150.0370.0230.0540.0440.0040.0080.0060.1160.1330.0700.0520.0730.0330.1770.1870.1340.1830.0950.0720.0080.0600.0150.0860.1300.1350.1510.1480.0250.0320.0400.0190.0320.0590.0300.0560.0260.0330.0460.0210.0521.0000.0190.0250.0240.0320.0470.0470.0110.0220.0380.0330.0440.016-0.0150.0610.0170.0210.0100.0360.0320.0290.0130.0300.2580.0240.0210.0190.0360.0090.0280.0200.3420.0200.0170.0360.0270.0200.0250.0280.0160.0250.0250.0220.0620.0100.0290.0360.0210.0330.3500.0110.0150.0200.2220.1850.0210.0140.0320.0160.0210.0200.0290.0160.0310.0210.027-0.0770.0610.054
make_Land Rover0.0090.0060.0180.0090.0000.0010.0330.0740.0490.0000.0010.1780.1170.0990.0620.0550.0220.0730.0880.0880.0380.0580.0200.0000.0310.0200.0400.1490.2290.2030.1460.0140.0200.0250.0100.0190.0360.0180.0350.0160.0200.0290.0120.0320.0191.0000.0140.0140.0200.0290.0290.0040.0120.0230.0200.0270.0080.0340.0180.0090.0120.0070.0220.0220.0170.0060.0130.0150.0140.0160.0110.0220.1020.0170.0160.0120.0110.1650.0220.0160.2540.0150.0170.0100.0140.0150.0120.1650.0030.0180.0110.2080.0070.0170.0040.0750.0110.0020.0120.0120.0080.0510.0080.0120.0110.1310.0080.0190.0120.0160.0660.032-0.025
make_Lexus0.0130.0100.0190.0130.0000.0060.0430.0140.0580.0050.0030.1570.1610.1870.0170.0130.0090.0910.1130.0800.0610.1900.0240.0050.0100.0440.0540.0860.1240.0740.0470.0190.0260.0320.0140.0250.0470.0240.0450.0210.0270.0370.0170.0420.0250.0141.0000.0190.0260.0370.0380.0080.0170.0300.0260.0350.0120.0300.0130.0090.0160.0220.0280.0290.0220.2550.1740.0170.0190.0220.0210.0170.0240.0220.0220.0810.0180.0130.0270.0270.0270.1970.0160.0450.0000.0380.0110.1090.0070.0230.0450.0360.0110.0220.0080.0560.0150.0070.0330.0780.0120.0320.0350.0150.0190.0170.0780.0250.0160.0180.0380.068-0.067
make_Lincoln0.0120.0090.0220.0120.0000.0050.0420.0740.0350.0040.0020.1640.1290.1060.0420.0380.0130.0590.0680.1400.0440.0650.0260.0000.0000.0250.1020.1250.1190.1560.1560.0190.0250.0310.0140.0240.0460.0230.0440.0200.0260.0360.0160.0400.0240.0140.0191.0000.0250.0360.0360.0070.0160.0290.0250.0340.011-0.0120.0230.4090.0150.0100.0270.0280.0220.0090.0180.0190.0240.0210.3260.0270.0140.0210.0210.0160.0150.0120.0130.1220.0270.0180.0210.3140.0190.0190.0160.1000.0060.0220.0150.0160.0100.0220.0070.0100.0150.0060.0120.0160.0750.0310.0110.0160.0150.0210.2760.0240.0150.0190.0930.0440.016
make_Mazda0.0170.0300.0270.0160.0000.0000.0560.0830.0530.0080.0070.2110.1990.0980.0550.0510.0150.1300.1490.1700.1570.1270.0350.0090.0130.0590.1000.0860.1010.1160.1250.0260.0340.0420.0190.0330.0610.0310.0590.0270.0350.0480.0220.0540.0320.0200.0260.0251.0000.0490.0490.0120.0230.0390.0340.0460.016-0.0700.0310.0180.0210.0150.0370.0380.0300.0140.0080.0260.0250.2170.0200.0370.0200.0290.0290.0220.0210.0180.0380.0280.0320.0220.0370.0160.0260.0260.0230.0150.0110.0310.0210.0220.0150.0460.0120.1390.5900.0100.0220.0200.0160.0420.0170.0220.0210.4000.0160.0320.0220.185-0.0950.0910.093
make_Mercedes-Benz0.2180.0350.0120.0000.0040.0960.0770.1120.1090.0010.0000.4020.3200.1810.0640.1220.1060.1010.1430.1700.1510.1350.0530.0000.0140.0310.0990.1530.1910.1150.1380.0370.0490.0600.0290.0470.0870.0450.0840.0400.0500.0690.0320.0770.0470.0290.0370.0360.0491.0000.0700.0180.0330.0560.0500.0660.0250.0210.0410.0870.1030.0140.0440.0430.0400.0200.0350.0290.0370.0770.2950.0650.0650.0260.0490.0680.0320.0680.0470.0250.0520.0120.0210.0240.0350.0400.0330.0530.0230.0440.0830.1150.2250.0370.0180.0220.0300.0150.0320.0320.0240.0470.0250.0820.0740.0330.1110.1890.0580.0380.2850.027-0.010
make_Nissan0.0120.0190.0160.0200.0040.0150.0210.0700.1320.0000.0010.2430.2390.1960.1500.1780.0320.1080.1220.1150.0680.1530.0000.0140.0370.0510.0910.4990.3550.5020.4970.0380.0490.0600.0290.0480.0880.0450.0850.0400.0500.0700.0330.0780.0470.0290.0380.0360.0490.0701.0000.0180.0330.0570.0500.0660.025-0.0850.0460.0920.0150.0220.0540.0350.4160.0200.2580.0380.0370.0410.0300.0540.2220.0430.0420.0280.0410.0260.0550.0790.0530.2580.0420.0250.0350.0380.0330.0230.0170.0450.0310.0320.0230.0430.0180.0230.0230.0170.1950.0320.0240.0610.0250.2680.0310.0940.0250.0470.0320.041-0.1430.0430.038
make_Porsche0.0020.0000.0530.0020.0000.0000.0210.0230.0300.0000.0000.0850.1320.0730.0210.0350.0230.0540.0390.0380.0430.0520.0070.0000.0000.0070.0330.0480.0470.0450.0480.0080.0110.0150.0040.0110.0230.0100.0220.0090.0120.0180.0060.0200.0110.0040.0080.0070.0120.0180.0181.0000.0060.0140.0120.0170.0010.0530.3460.0400.0050.0000.0130.0130.0140.0000.0070.0080.0070.0090.0040.0130.0040.0090.0090.0090.0050.0040.0090.0090.0130.0080.0090.0000.0080.0080.0060.0000.0000.0100.0050.0050.0000.0100.0000.0000.0050.0000.4000.0050.0000.0130.1170.0040.0020.0100.0000.0110.0050.0090.0060.082-0.050
make_RAM0.0670.0080.0200.0110.0000.0040.3830.1870.0560.0030.0220.4000.3180.2680.0450.0360.0240.1770.1480.1700.1890.1430.0230.0030.0350.0240.0870.0980.1300.1410.1120.0170.0220.0280.0120.0220.0420.0210.0400.0180.0230.0330.0140.0370.0220.0120.0170.0160.0230.0330.0330.0061.0000.0260.0230.0310.0100.0010.0210.0110.0160.0080.0250.0250.0200.0080.1470.0170.0160.0190.0130.0250.0130.0190.0190.0140.0550.0110.0250.0180.0240.0170.6510.0100.0170.0170.0150.0090.0050.0200.0140.0570.0090.0200.0060.0090.0130.0050.0140.0140.0100.0280.2900.0140.0140.0200.0100.0220.0000.0180.1080.0190.051
make_Subaru0.0200.0170.0340.0740.0000.0120.0650.0700.0300.0000.0060.1420.1140.1570.1190.1050.0430.1490.1740.1220.1090.2100.0360.0110.0570.0400.1290.1190.2010.2030.1520.0300.0390.0480.0230.0380.0710.0360.0680.0320.0400.0560.0260.0620.0380.0230.0300.0290.0390.0560.0570.0140.0261.0000.0400.0530.019-0.0660.0370.0180.0250.0150.0430.1730.1330.0150.0280.0300.0290.0330.0230.0430.0230.5990.0330.0260.0240.0210.0250.0320.0420.0310.0340.0190.0300.0300.0270.0180.0130.0360.0250.0250.0180.0710.0000.0180.0240.0130.0250.0250.0190.0490.0200.0260.0250.0350.0190.0370.0700.355-0.0540.0400.060
make_Toyota0.0180.0130.0060.0150.0030.0040.0250.0390.0310.0000.0030.1330.0550.0820.0460.0500.0000.0680.1850.1540.0890.1360.0270.0090.0880.1560.0780.1420.0930.0390.0760.0260.0340.0430.0200.0340.0620.0320.0600.0280.0350.0490.0230.0550.0330.0200.0260.0250.0340.0500.0500.0120.0230.0401.0000.0470.0170.0630.0320.0000.2670.0150.0380.0390.0310.0110.0250.2040.0190.0290.0400.0230.0200.0300.1250.0180.0660.2190.0380.0210.0300.0200.0300.0320.0210.0270.0200.0160.0230.0310.0130.1600.0150.0280.0120.0260.0210.0110.0220.0220.0190.0430.0170.1640.0220.0260.0170.0470.0220.022-0.0580.093-0.089
make_Volkswagen0.0240.0160.0400.0220.0030.0140.0760.0260.0390.0740.0770.2240.2560.2080.0110.0350.0430.1990.3030.1630.1370.1180.0310.0130.0590.0480.1020.1420.0960.1050.1830.0350.0460.0560.0270.0450.0820.0420.0790.0370.0470.0650.0300.0730.0440.0270.0350.0340.0460.0660.0660.0170.0310.0530.0471.0000.023-0.0810.0430.0250.0290.0170.0500.0630.0410.0200.0330.0360.0150.0390.0280.2080.0430.0400.0390.0180.0290.0230.0510.0360.0490.0360.0260.0230.0550.4450.0310.0220.0160.0410.0230.0300.0190.0400.0170.0210.0290.0150.0300.0300.0230.4420.0230.0300.0290.0300.0230.2750.0300.038-0.1140.0940.087
make_Volvo0.0070.0000.0150.0070.0000.0000.0280.0020.0320.0760.0000.1080.1070.1430.0360.0290.0190.0410.0350.0280.0300.0430.0000.0000.0540.0830.0630.2040.1290.1520.1310.0120.0160.0210.0080.0160.0310.0150.0300.0130.0170.0240.0100.0270.0160.0080.0120.0110.0160.0250.0250.0010.0100.0190.0170.0231.0000.0080.0150.0070.0220.0000.0540.0190.0140.0130.0110.0120.0120.0220.0060.0140.0080.0140.0140.0100.2640.0070.0190.0130.0180.0120.0140.0060.0120.2000.0100.0050.0000.0150.2230.0090.0070.0120.0010.0050.0090.0000.0410.0090.0060.0950.0060.0100.0090.0140.2140.0160.0090.0130.0310.030-0.017
mileage-0.0210.0400.0870.006-0.026-0.007-0.014-0.1430.1380.0600.0130.0150.0050.063-0.1030.0740.072-0.0310.0090.0250.0320.014-0.1080.0820.108-0.072-0.0550.0550.0470.028-0.0220.0450.0750.0890.0240.0010.0170.007-0.0120.0310.050-0.0790.017-0.000-0.0150.0340.030-0.012-0.0700.021-0.0850.0530.001-0.0660.063-0.0810.0081.0000.0130.0160.0190.0000.0000.0000.0000.0100.0130.0000.0000.0000.0000.0230.0200.0000.0190.0000.0000.0110.0100.0200.0000.0080.0170.0000.0120.0000.0090.0000.0000.0180.0060.0270.0780.0000.0080.0050.0150.0050.0000.0510.0140.0320.0230.0000.0160.0070.0130.0000.0130.015-0.2630.282-0.859
model_hashed_00.0160.0130.0550.0470.0000.0080.3930.1940.0690.0090.0000.3040.2380.1800.0780.0760.0200.0500.0350.0340.0430.0430.0320.0190.0260.0110.0450.0740.0480.0760.0700.0240.0700.0290.0160.0310.0520.0280.2000.3170.0320.0450.0210.0500.0610.0180.0130.0230.0310.0410.0460.3460.0210.0370.0320.0430.0150.0131.0000.0110.0130.0080.0240.0240.0190.0080.0150.0160.0160.0180.0120.0240.0120.0190.0180.0130.0130.0100.0240.0180.0230.0170.0180.0090.0160.0160.0140.0090.0040.0190.0130.0130.0080.0190.0110.0080.0130.0040.0130.0130.0090.0270.0090.0130.0130.0190.0090.0210.0130.0180.0970.086-0.004
model_hashed_10.0270.0060.0160.0310.0000.2640.0310.0820.0510.0270.0000.1320.0740.0490.0920.0440.1090.0450.0480.0620.0310.0380.0590.0000.0680.0410.0350.0550.0680.0740.0750.0130.0180.0150.0090.0170.0200.0160.0290.0140.0190.0260.0110.0300.0170.0090.0090.4090.0180.0870.0920.0400.0110.0180.0000.0250.0070.0160.0111.0000.0050.0000.0130.0130.0100.0000.0070.0080.0070.0090.0040.0130.0040.0100.0090.0060.0050.0020.0130.0090.0130.0080.0090.0000.0080.0080.0060.0000.0000.0100.0050.0050.0000.0100.0020.0000.0050.0000.0050.0050.0000.0150.0000.0060.0050.0100.0000.0110.0050.0090.0350.0350.031
model_hashed_100.0050.0000.0120.0100.0000.0030.0700.0560.0360.0430.0360.1630.0880.1750.0460.0420.0170.0300.0350.0450.0670.0370.0220.0140.0470.0870.0470.0660.0530.0410.0370.0160.0210.0270.0110.0210.0400.0170.0380.0170.0220.0310.0130.0860.0210.0120.0160.0150.0210.1030.0150.0050.0160.0250.2670.0290.0220.0190.0130.0051.0000.0020.0160.0160.0120.0010.0090.0100.0100.0110.0070.0160.0070.0120.0120.0080.0070.0050.0160.0110.0160.0100.0120.0040.0100.0100.0080.0030.0000.0130.0070.0080.0030.0120.0050.0030.0070.0000.0080.0080.0040.0180.0040.0080.0070.0120.0040.0130.0080.0110.0330.0540.043
model_hashed_110.0050.1290.0550.0050.0000.0000.0240.0420.0330.0670.0000.0790.1410.0960.0340.0260.0260.0230.0210.0160.0220.0180.0230.0000.0450.0630.0250.0360.0470.0420.0270.0080.1630.0370.2020.0310.0280.0130.0260.0110.0150.0390.0050.0250.0100.0070.0220.0100.0150.0140.0220.0000.0080.0150.0150.0170.0000.0000.0080.0000.0021.0000.0100.0110.0070.0000.0040.0050.0050.0060.0000.0100.0000.0070.0070.0020.0010.0000.0100.0060.0100.0060.0070.0000.0050.0050.0030.0000.0000.0070.0020.0020.0000.0070.0000.0000.0000.0000.0020.0020.0000.0120.0000.0020.0020.0070.0000.0080.0020.0060.0080.034-0.011
model_hashed_120.0310.0160.0330.0190.0000.0110.0610.1230.0830.0100.0040.3340.0910.2690.0780.0780.0100.1200.1040.1350.1440.1200.0380.0280.0480.0390.0630.1000.1610.0940.1050.0280.0370.0460.0220.0360.0670.5510.0500.0300.0380.1610.0240.3670.0360.0220.0280.0270.0370.0440.0540.0130.0250.0430.0380.0500.0540.0000.0240.0130.0160.0101.0000.0290.0230.0100.0180.0200.0190.0210.0150.0280.0150.0220.0220.0160.0150.0130.0290.0210.0280.0200.0220.0120.0190.0200.0170.0110.0070.0230.0160.0160.0110.0220.0130.0110.0150.0070.0160.0160.0120.0320.0120.0160.0160.0230.0120.0250.0160.0210.0160.056-0.033
model_hashed_130.0120.0160.0280.0140.0000.0110.1050.0990.0800.0100.0050.1890.1190.1780.1490.1520.0230.0700.0530.0630.0490.0440.0320.0140.0490.0380.1360.1860.1500.1500.1510.0290.0290.0440.1250.0370.3800.0300.1710.0310.0370.0530.0250.0600.0320.0220.0290.0280.0380.0430.0350.0130.0250.1730.0390.0630.0190.0000.0240.0130.0160.0110.0291.0000.0230.0100.0190.0200.0190.0220.0150.0290.0150.0230.0220.0170.0160.0130.0290.0210.0280.0200.0220.0120.0200.0200.0170.0110.0070.0240.0160.0170.0110.0230.0130.0110.0160.0070.0160.0170.0120.0330.0120.0170.0160.0230.0120.0250.0160.0220.0800.048-0.018
model_hashed_140.0080.0170.0220.0170.0000.0080.0440.0520.0500.0070.0070.1310.1120.0800.0440.0600.1310.0420.0490.0510.0410.0620.0310.0000.0410.0250.0590.1610.1420.2110.1890.0230.1670.0320.0170.0300.0540.1530.0440.0250.0310.0430.0200.0490.0290.0170.0220.0220.0300.0400.4160.0140.0200.1330.0310.0410.0140.0000.0190.0100.0120.0070.0230.0231.0000.0070.0140.0160.0150.0170.0110.0230.0110.0180.0170.0130.0120.0100.0230.0170.0230.0160.0180.0090.0150.0160.0130.0080.0040.0190.0120.0130.0080.0180.0100.0080.0120.0030.0130.0130.0090.0260.0090.0130.0120.0180.0090.0200.0130.017-0.0250.0300.025
model_hashed_150.0580.0000.0610.0150.0000.0000.0250.0750.0940.0280.0090.1190.1570.1150.0850.0970.0100.0390.0400.0500.0410.0490.0150.0000.0190.0210.0280.0440.0620.0570.0520.0100.0140.0270.0060.0140.0270.0130.0260.0110.0150.0160.0190.1680.0130.0060.2550.0090.0140.0200.0200.0000.0080.0150.0110.0200.0130.0100.0080.0000.0010.0000.0100.0100.0071.0000.0040.0050.0040.0060.0000.0100.0000.0070.0060.0020.0000.0000.0100.0060.0100.0050.0060.0000.0050.0050.0030.0000.0000.0070.0010.0020.0000.0070.0000.0000.0000.0000.0010.0020.0000.0120.0000.0020.0010.0070.0000.0080.0010.0060.0070.0670.044
model_hashed_160.0120.0150.0550.0120.0000.0050.1250.1910.1590.0030.0000.1160.0990.0530.0480.0540.0000.0480.0420.0460.0220.0640.0250.0000.0350.0300.0420.1040.0690.1020.1260.0180.0150.0260.0130.0240.0440.0220.0260.0190.0120.0350.0770.0390.0300.0130.1740.0180.0080.0350.2580.0070.1470.0280.0250.0330.0110.0130.0150.0070.0090.0040.0180.0190.0140.0041.0000.0120.0110.0130.0080.0180.0080.0140.0140.0090.0090.0070.0180.0130.0180.0120.0140.0060.0120.0120.0100.0050.0000.0150.0090.0090.0050.0140.0070.0050.0090.0000.0090.0090.0060.0210.0060.0090.0090.0140.0060.0160.0090.0130.0260.038-0.022
model_hashed_170.0280.0490.1570.0090.0000.0060.0390.0970.0450.0000.0000.1850.0630.1620.0390.0560.1490.0430.0940.0570.0480.0400.0250.0050.0780.1210.0400.0660.0600.0690.0670.0200.0160.2220.0150.0250.0390.0240.0980.0210.0270.0350.0120.0380.2580.0150.0170.0190.0260.0290.0380.0080.0170.0300.2040.0360.0120.0000.0160.0080.0100.0050.0200.0200.0160.0050.0121.0000.0130.0140.0090.0200.0090.0150.0150.0100.0100.0080.0200.0140.0190.0130.0150.0070.0130.0130.0110.0060.0000.0160.0100.0100.0060.0150.0080.0060.0100.0000.0100.0100.0070.0220.0070.0100.0100.0150.0070.0170.0100.0140.0110.052-0.066
model_hashed_180.0120.0090.0230.0120.0000.0050.0420.0800.0440.0040.0000.1350.0950.1580.0580.0600.0170.0620.0300.0380.0360.0400.0270.0050.0330.0210.0400.1330.1870.1750.1030.0190.0250.0310.1560.0240.0660.0230.2880.0200.0260.0700.0160.0410.0240.0140.0190.0240.0250.0370.0370.0070.0160.0290.0190.0150.0120.0000.0160.0070.0100.0050.0190.0190.0150.0040.0110.0131.0000.0140.0090.0190.0090.0140.0140.0100.0090.0070.0190.0140.0190.0130.0140.0060.0120.0120.0110.0060.0000.0150.0100.0100.0050.0150.0070.0050.0090.0000.0100.0100.0060.0220.0060.0100.0100.0150.0060.0160.0100.0140.0090.0360.039
model_hashed_190.0140.0150.0240.0040.0000.0090.0790.1390.1390.0060.0040.1090.1940.1450.0990.1190.0250.0440.0420.0400.0650.0470.0250.0060.0400.0300.0950.0940.0940.1040.0910.0220.0280.0510.0440.0280.1960.0260.0450.0230.0290.0410.0180.0830.0210.0160.0220.0210.2170.0770.0410.0090.0190.0330.0290.0390.0220.0000.0180.0090.0110.0060.0210.0220.0170.0060.0130.0140.0141.0000.0100.0210.0110.0170.0160.0120.0110.0090.0220.0160.0210.0150.0160.0080.0140.0140.0120.0070.0020.0170.0110.0120.0070.0170.0090.0070.0110.0020.0120.0120.0080.0240.0080.0120.0110.0170.0080.0180.0120.016-0.0380.000-0.001
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model_hashed_40.0050.0170.0170.0490.0000.0000.0260.1000.1180.0500.0100.2630.1290.1810.0310.0330.0140.0300.0380.0290.0360.0260.0160.0000.0050.0000.0430.0340.0670.0520.0360.0110.0250.0680.0070.0100.0290.0140.0210.0120.0160.0230.2120.0250.0330.0070.0110.0100.0150.2250.0230.0000.0090.0180.0150.0190.0070.0780.0080.0000.0030.0000.0110.0110.0080.0000.0050.0060.0050.0070.0000.0110.0010.0070.0070.0030.0020.0000.0110.0070.0100.0060.0070.0000.0060.0060.0040.0000.0000.0080.0030.0031.0000.0070.0000.0000.0020.0000.0030.0030.0000.0130.0000.0030.0030.0080.0000.0090.0030.007-0.0280.0610.006
model_hashed_400.0150.0120.0270.0150.0000.0070.0490.0840.0750.0070.0020.0920.0440.0990.0460.0540.0340.0590.0550.0450.0700.0350.0290.0070.0250.0600.0770.0820.0850.0880.0830.0220.0140.0230.0170.0280.1360.0270.0240.0240.0490.0420.3310.0480.3500.0170.0220.0220.0460.0370.0430.0100.0200.0710.0280.0400.0120.0000.0190.0100.0120.0070.0220.0230.0180.0070.0140.0150.0150.0170.0110.0220.0110.0170.0170.0120.0120.0090.0230.0160.0220.0160.0170.0080.0150.0150.0130.0080.0030.0180.0120.0120.0071.0000.0100.0080.0120.0030.0120.0120.0080.0260.0090.0120.0120.0180.0080.0190.0120.016-0.0340.096-0.083
model_hashed_410.0020.0000.0100.0020.0000.0000.0210.0260.0090.0000.0470.0940.0640.0670.0310.0250.0140.1200.0330.0440.0310.1630.0120.0000.0200.0120.0680.0570.0460.0480.0470.0080.0120.0080.0040.3250.0230.0100.0000.0090.0120.0180.0060.0200.0110.0040.0080.0070.0120.0180.0180.0000.0060.0000.0120.0170.0010.0080.0110.0020.0050.0000.0130.0130.0100.0000.0070.0080.0070.0090.0040.0130.0040.0090.0090.0060.0050.0020.0130.0090.0130.0080.0090.0000.0080.0080.0060.0000.0000.0100.0050.0050.0000.0101.0000.0000.0050.0000.0050.0050.0000.0150.0000.0060.0050.0100.0000.0110.0050.0090.0480.0120.018
model_hashed_420.0050.0030.0120.0920.0000.0000.0270.0290.0170.0000.0000.1200.0420.1470.0290.0290.0080.0310.0350.0290.0330.0310.0100.0000.0090.0160.0320.0510.0520.0270.0400.0110.0150.0180.0070.0150.0750.0140.0280.0120.0160.0230.0240.1030.0150.0750.0560.0100.1390.0220.0230.0000.0090.0180.0260.0210.0050.0050.0080.0000.0030.0000.0110.0110.0080.0000.0050.0060.0050.0070.0010.0110.0010.0070.0070.0030.0020.0000.0110.0070.0110.0060.0070.0000.0060.0060.0040.0000.0000.0080.0030.0030.0000.0080.0001.0000.0020.0000.0030.0030.0000.0130.0000.0030.0030.0080.0000.0090.0030.007-0.0620.027-0.018
model_hashed_430.0090.0070.0060.0100.0000.0020.0350.0760.0520.0000.0000.1180.1110.0760.0590.0560.0130.0770.0880.0900.0910.0560.0210.0000.0660.0910.0420.0470.0560.0580.1000.0150.0950.0260.0110.0200.0380.0190.0280.0170.0210.0300.0870.0340.0200.0110.0150.0150.5900.0300.0230.0050.0130.0240.0210.0290.0090.0150.0130.0050.0070.0000.0150.0160.0120.0000.0090.0100.0090.0110.0060.0150.0060.0110.0110.0070.0070.0040.0160.0110.0150.0100.0110.0030.0090.0100.0080.0020.0000.0120.0070.0070.0020.0120.0050.0021.0000.0000.0070.0070.0030.0180.0040.0070.0070.0120.0030.0130.0070.0110.0940.079-0.075
model_hashed_440.0000.1350.0610.0000.0000.0000.0300.0540.0130.0000.0000.1060.1450.0680.0190.0270.0650.0380.0780.1180.1200.0260.2210.0000.1900.0510.0240.0410.0360.0460.0450.0070.0100.1130.0020.0100.0210.0090.0200.0630.0520.0080.0040.0190.2220.0020.0070.0060.0100.0150.0170.0000.0050.0130.0110.0150.0000.0050.0040.0000.0000.0000.0070.0070.0030.0000.0000.0000.0000.0020.0000.0070.0000.0030.0020.0000.0000.0000.0070.0010.0060.0000.0030.0000.0000.0000.0000.0000.0000.0040.0000.0000.0000.0030.0000.0000.0001.0000.0000.0000.0000.0080.0000.0000.0000.0030.0000.0050.0000.002-0.0010.0490.045
model_hashed_450.0100.0000.0160.0070.0000.0030.0320.1440.1960.0000.0000.0940.0790.2500.1560.1630.0150.0260.0560.0480.0350.0450.0150.0010.0290.0220.0290.0710.0570.0620.0430.0160.0210.0210.0120.0210.0350.0200.0380.0170.0220.0310.0130.0510.1850.0120.0330.0120.0220.0320.1950.4000.0140.0250.0220.0300.0410.0000.0130.0050.0080.0020.0160.0160.0130.0010.0090.0100.0100.0120.0070.0160.0070.0120.0120.0080.0070.0050.0160.0110.0160.0110.0120.0040.0100.0100.0080.0030.0000.0130.0070.0080.0030.0120.0050.0030.0070.0001.0000.0080.0040.0180.0040.0080.0070.0120.0040.0140.0080.0110.1160.053-0.037
model_hashed_460.0100.0210.0030.0710.0000.0030.1650.1140.0890.0000.0000.1490.1410.3060.1110.1280.0170.0500.0450.0370.0340.0380.0220.0000.0240.0240.0260.0370.0640.0480.0560.0160.0220.0200.0130.0170.0390.0200.0390.0990.2920.1260.0520.0300.0210.0120.0780.0160.0200.0320.0320.0050.0140.0250.0220.0300.0090.0510.0130.0050.0080.0020.0160.0170.0130.0020.0090.0100.0100.0120.0070.0160.0070.0120.0120.0080.0070.0050.0160.0110.0160.0110.0120.0040.0100.0100.0090.0030.0000.0130.0080.0080.0030.0120.0050.0030.0070.0000.0081.0000.0040.0190.0040.0080.0080.0120.0040.0140.0080.011-0.0590.070-0.069
model_hashed_470.0060.0350.1260.0060.0000.0000.0280.0220.0420.0000.0000.1210.0840.1370.0610.0040.1150.0230.0260.0240.0150.0250.0170.0000.0260.0170.0590.0690.0500.0420.0560.0120.0160.0200.0080.0160.2160.0150.0290.0130.1170.0240.0090.0090.0140.0080.0120.0750.0160.0240.0240.0000.0100.0190.0190.0230.0060.0140.0090.0000.0040.0000.0120.0120.0090.0000.0060.0070.0060.0080.0020.0120.0030.0080.0080.0040.0030.0000.0120.0070.0110.0070.0080.0000.0060.0060.0050.0000.0000.0090.0040.0040.0000.0080.0000.0000.0030.0000.0040.0041.0000.0140.0000.0040.0040.0080.0000.0090.0040.0070.0220.0610.045
model_hashed_480.0220.0150.0330.0220.0550.0220.0660.1400.0990.0030.0540.1650.1620.1550.0920.1110.0390.0860.1170.0630.0490.0510.0890.0040.0570.0320.1160.1260.1460.1270.1510.0320.0340.0140.1160.0410.3050.0340.0550.0340.0420.0350.0280.0400.0320.0510.0320.0310.0420.0470.0610.0130.0280.0490.0430.4420.0950.0320.0270.0150.0180.0120.0320.0330.0260.0120.0210.0220.0220.0240.0170.0320.0170.0250.0250.0190.0180.0150.0330.0240.0320.0230.0250.0140.0220.0220.0190.0130.0080.0260.0180.0190.0130.0260.0150.0130.0180.0080.0180.0190.0141.0000.0140.0190.0180.0260.0140.0280.0180.0240.0160.0850.078
model_hashed_490.0060.0000.0450.0040.0000.0000.1230.0670.0280.0000.0000.1210.1140.1170.0270.0240.0160.0600.0520.0620.0560.0570.0170.0000.0220.0110.0350.0420.0450.0450.0420.0120.0670.0120.0080.0070.0300.0150.0220.0130.3020.0240.0070.0280.0160.0080.0350.0110.0170.0250.0250.1170.2900.0200.0170.0230.0060.0230.0090.0000.0040.0000.0120.0120.0090.0000.0060.0070.0060.0080.0030.0120.0030.0080.0080.0040.0040.0000.0120.0080.0120.0070.0080.0000.0070.0070.0050.0000.0000.0090.0040.0040.0000.0090.0000.0000.0040.0000.0040.0040.0000.0141.0000.0040.0040.0090.0000.0100.0040.0080.0480.0530.020
model_hashed_50.0100.0080.0180.0000.0000.0030.0560.0110.0490.0010.0000.1340.1110.0970.1920.2170.0210.0260.0320.0530.0290.0510.0230.0020.0300.0190.0510.1300.1030.1510.1330.0160.0220.0270.0120.0190.0400.0200.0550.0180.0230.0280.0100.0360.0210.0120.0150.0160.0220.0820.2680.0040.0140.0260.1640.0300.0100.0000.0130.0060.0080.0020.0160.0170.0130.0020.0090.0100.0100.0120.0070.0160.0070.0120.0120.0080.0070.0050.0170.0110.0160.0110.0120.0040.0100.0100.0090.0040.0000.0130.0080.0080.0030.0120.0060.0030.0070.0000.0080.0080.0040.0190.0041.0000.0080.0120.0040.0140.0080.012-0.1410.0150.007
model_hashed_500.0100.0140.0070.0100.0000.0030.0360.0800.0870.0170.0000.1210.1150.1250.0310.0350.0120.0370.0380.0410.0970.0540.0180.0170.0260.0330.1200.1190.1030.0940.1120.0160.1660.0290.0110.1390.0390.0190.0380.4280.0220.0310.0070.0350.0200.0110.0190.0150.0210.0740.0310.0020.0140.0250.0220.0290.0090.0160.0130.0050.0070.0020.0160.0160.0120.0010.0090.0100.0100.0110.0070.0160.0070.0120.0120.0080.0070.0050.0160.0110.0150.0100.0120.0040.0100.0100.0080.0030.0000.0130.0070.0080.0030.0120.0050.0030.0070.0000.0070.0080.0040.0180.0040.0081.0000.0120.0040.0130.0070.0110.0520.0750.022
model_hashed_510.0150.0640.0090.0880.0000.0080.0410.0580.0560.0060.0000.1610.1060.0710.0590.0600.0270.1250.0480.0560.0480.0710.2180.0070.1350.0260.0460.0830.1010.0890.0530.0230.0940.0320.0170.2310.0540.0270.0520.0180.0310.0430.1580.0480.0290.1310.0170.0210.4000.0330.0940.0100.0200.0350.0260.0300.0140.0070.0190.0100.0120.0070.0230.0230.0180.0070.0140.0150.0150.0170.0110.0220.0110.0170.0170.0120.0120.0090.0230.0160.0220.0160.0170.0080.0150.0150.0130.0080.0030.0180.0120.0120.0080.0180.0100.0080.0120.0030.0120.0120.0080.0260.0090.0120.0121.0000.0080.0190.0120.017-0.0730.0290.014
model_hashed_60.0060.0220.0000.0070.0000.0000.0280.0130.0320.0050.0000.1030.0790.0930.0310.0260.0140.0210.0100.0330.0200.0340.0170.0000.0670.1020.0370.0420.0390.0490.0280.0550.0160.0160.0080.0160.0310.0150.0300.0130.0170.0180.0100.0270.0160.0080.0780.2760.0160.1110.0250.0000.0100.0190.0170.0230.2140.0130.0090.0000.0040.0000.0120.0120.0090.0000.0060.0070.0060.0080.0030.0120.0030.0080.0080.0040.0030.0000.0120.0080.0110.0070.0080.0000.0060.0070.0050.0000.0000.0090.0040.0040.0000.0080.0000.0000.0030.0000.0040.0040.0000.0140.0000.0040.0040.0081.0000.0100.0040.008-0.0030.0340.011
model_hashed_70.4440.0130.2170.0090.0000.1040.0310.1860.1360.0190.0000.3780.1530.2770.1960.1340.2710.0610.0660.0470.0540.0610.2460.0080.1540.0220.0550.0790.0610.0740.0790.0250.0450.0400.0190.0310.0580.2140.1330.0230.0330.0460.0210.0520.0310.0190.0250.0240.0320.1890.0470.0110.0220.0370.0470.2750.0160.0000.0210.0110.0130.0080.0250.0250.0200.0080.0160.0170.0160.0180.0120.0240.0120.0190.0190.0140.0130.0110.0250.0180.0240.0170.0190.0100.0160.0170.0140.0090.0050.0200.0130.0140.0090.0190.0110.0090.0130.0050.0140.0140.0090.0280.0100.0140.0130.0190.0101.0000.0140.0180.1130.0680.020
model_hashed_80.0000.1090.0890.1570.0000.0030.0340.0540.0460.0000.0000.1510.0790.1020.0340.0270.0510.1210.0300.0600.0220.1230.0220.0000.0320.0220.0770.0480.0480.0390.0370.0160.0210.1260.0120.4080.0400.0200.0380.0170.0220.0310.0130.0350.0210.0120.0160.0150.0220.0580.0320.0050.0000.0700.0220.0300.0090.0130.0130.0050.0080.0020.0160.0160.0130.0010.0090.0100.0100.0120.0070.0160.0070.0120.0120.0080.0070.0050.0160.0110.0160.0110.0120.0040.0100.0100.0080.0030.0000.0130.0070.0080.0030.0120.0050.0030.0070.0000.0080.0080.0040.0180.0040.0080.0070.0120.0040.0141.0000.0110.0140.0400.033
model_hashed_90.0140.0110.0190.0140.0000.0070.0470.0860.0930.0060.0040.1160.0640.0910.0650.0570.0290.0610.0500.0650.0390.0680.0290.0060.0890.1480.0690.0680.0990.0960.0550.0210.0280.0270.0160.0270.0510.0260.0460.0230.0290.1680.0180.0420.0270.0160.0180.0190.1850.0380.0410.0090.0180.3550.0220.0380.0130.0150.0180.0090.0110.0060.0210.0220.0170.0060.0130.0140.0140.0160.0100.0210.0100.0160.0160.0120.0110.0090.0210.0150.0210.0150.0160.0080.0140.0140.0120.0070.0020.0170.0110.0110.0070.0160.0090.0070.0110.0020.0110.0110.0070.0240.0080.0120.0110.0170.0080.0180.0111.0000.0190.0400.040
msrp0.0780.0430.102-0.004-0.0020.0140.1700.013-0.216-0.026-0.0090.1460.0280.2750.484-0.5650.0880.0050.063-0.0530.113-0.0120.198-0.024-0.2030.0980.004-0.095-0.0590.080-0.029-0.0250.0980.125-0.0850.082-0.1480.0480.0110.014-0.099-0.1440.0290.135-0.0770.0660.0380.093-0.0950.285-0.1430.0060.108-0.054-0.058-0.1140.031-0.2630.0970.0350.0330.0080.0160.080-0.0250.0070.0260.0110.009-0.038-0.0160.069-0.0300.027-0.032-0.0230.006-0.014-0.0310.008-0.0250.078-0.0410.0400.0690.0430.058-0.054-0.0240.045-0.0810.029-0.028-0.0340.048-0.0620.094-0.0010.116-0.0590.0220.0160.048-0.1410.052-0.073-0.0030.1130.0140.0191.0000.2100.313
stock_type0.0260.0540.1140.0040.0190.0080.0170.1630.1540.0550.0000.2170.1740.1700.1230.0900.0840.1180.1020.1190.0960.1190.1190.0660.1160.0670.0780.1110.1030.0560.0870.0480.0790.1040.0360.0040.0600.0330.0280.0440.0720.1120.0120.0520.0610.0320.0680.0440.0910.0270.0430.0820.0190.0400.0930.0940.0300.2820.0860.0350.0540.0340.0560.0480.0300.0670.0380.0520.0360.0000.0100.0980.0000.0480.0710.0290.0320.0750.0720.0260.0060.0540.0620.0250.0150.0370.0260.0280.0280.0440.0260.0410.0610.0960.0120.0270.0790.0490.0530.0700.0610.0850.0530.0150.0750.0290.0340.0680.0400.0400.2101.0000.882
year0.023-0.056-0.148-0.0040.016-0.0070.0230.186-0.163-0.076-0.013-0.0270.003-0.0330.164-0.108-0.1330.033-0.002-0.023-0.034-0.0200.088-0.094-0.0900.0710.043-0.064-0.0410.012-0.016-0.032-0.074-0.050-0.0490.002-0.049-0.004-0.022-0.036-0.0570.103-0.0240.0310.054-0.025-0.0670.0160.093-0.0100.038-0.0500.0510.060-0.0890.087-0.017-0.859-0.0040.0310.043-0.011-0.033-0.0180.0250.044-0.022-0.0660.039-0.001-0.0090.072-0.012-0.050-0.0640.015-0.031-0.0650.0630.015-0.0060.0300.029-0.031-0.007-0.034-0.007-0.019-0.021-0.030-0.0050.0030.006-0.0830.018-0.018-0.0750.045-0.037-0.0690.0450.0780.0200.0070.0220.0140.0110.0200.0330.0400.3130.8821.000

Missing values

2024-05-20T00:01:54.030577image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-20T00:01:54.995292image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

msrpyearmileagestock_typemodel_hashed_0model_hashed_1model_hashed_2model_hashed_3model_hashed_4model_hashed_5model_hashed_6model_hashed_7model_hashed_8model_hashed_9model_hashed_10model_hashed_11model_hashed_12model_hashed_13model_hashed_14model_hashed_15model_hashed_16model_hashed_17model_hashed_18model_hashed_19model_hashed_20model_hashed_21model_hashed_22model_hashed_23model_hashed_24model_hashed_25model_hashed_26model_hashed_27model_hashed_28model_hashed_29model_hashed_30model_hashed_31model_hashed_32model_hashed_33model_hashed_34model_hashed_35model_hashed_36model_hashed_37model_hashed_38model_hashed_39model_hashed_40model_hashed_41model_hashed_42model_hashed_43model_hashed_44model_hashed_45model_hashed_46model_hashed_47model_hashed_48model_hashed_49model_hashed_50model_hashed_51exterior_color_x0exterior_color_x1exterior_color_x2exterior_color_x3exterior_color_x4interior_color_x0interior_color_x1interior_color_x2interior_color_x3interior_color_x4drivetrain_All-wheel Drivedrivetrain_Front-wheel Drivedrivetrain_Rear-wheel Drivemake_Acuramake_Audimake_BMWmake_Buickmake_Cadillacmake_Chevroletmake_Dodgemake_Fordmake_GMCmake_Hondamake_Hyundaimake_INFINITImake_Jeepmake_Kiamake_Land Rovermake_Lexusmake_Lincolnmake_Mazdamake_Mercedes-Benzmake_Nissanmake_Porschemake_RAMmake_Subarumake_Toyotamake_Volkswagenmake_Volvobodystyle_Cargo Vanbodystyle_Convertiblebodystyle_Coupebodystyle_Hatchbackbodystyle_Minivanbodystyle_Passenger Vanbodystyle_Pickup Truckbodystyle_SUVbodystyle_Sedanbodystyle_Wagonbodystyle_nancat_x0cat_x1cat_x2fuel_type_Electricfuel_type_Flexiblefuel_type_Gasolinefuel_type_Hybrid
057215.02024.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0-1.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0-1.0167430.0797230.957974-0.333534-0.814538-0.4942650.3875350.5897870.338319-0.4987021.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.0-0.049847-0.2997800.8461711.00.00.00.0
127995.02024.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0-1.00.00.00.01.2568520.7106460.2375720.141691-1.756619-0.6084760.7282570.914057-0.7876330.1766800.01.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.0-0.012299-0.2321880.8892740.00.01.00.0
283630.02024.020.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0-1.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.1940030.3499300.440473-0.011486-1.480162-0.6090010.7572460.397062-0.2834820.0908351.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.0-0.049847-0.2997800.8461711.00.00.00.0
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msrpyearmileagestock_typemodel_hashed_0model_hashed_1model_hashed_2model_hashed_3model_hashed_4model_hashed_5model_hashed_6model_hashed_7model_hashed_8model_hashed_9model_hashed_10model_hashed_11model_hashed_12model_hashed_13model_hashed_14model_hashed_15model_hashed_16model_hashed_17model_hashed_18model_hashed_19model_hashed_20model_hashed_21model_hashed_22model_hashed_23model_hashed_24model_hashed_25model_hashed_26model_hashed_27model_hashed_28model_hashed_29model_hashed_30model_hashed_31model_hashed_32model_hashed_33model_hashed_34model_hashed_35model_hashed_36model_hashed_37model_hashed_38model_hashed_39model_hashed_40model_hashed_41model_hashed_42model_hashed_43model_hashed_44model_hashed_45model_hashed_46model_hashed_47model_hashed_48model_hashed_49model_hashed_50model_hashed_51exterior_color_x0exterior_color_x1exterior_color_x2exterior_color_x3exterior_color_x4interior_color_x0interior_color_x1interior_color_x2interior_color_x3interior_color_x4drivetrain_All-wheel Drivedrivetrain_Front-wheel Drivedrivetrain_Rear-wheel Drivemake_Acuramake_Audimake_BMWmake_Buickmake_Cadillacmake_Chevroletmake_Dodgemake_Fordmake_GMCmake_Hondamake_Hyundaimake_INFINITImake_Jeepmake_Kiamake_Land Rovermake_Lexusmake_Lincolnmake_Mazdamake_Mercedes-Benzmake_Nissanmake_Porschemake_RAMmake_Subarumake_Toyotamake_Volkswagenmake_Volvobodystyle_Cargo Vanbodystyle_Convertiblebodystyle_Coupebodystyle_Hatchbackbodystyle_Minivanbodystyle_Passenger Vanbodystyle_Pickup Truckbodystyle_SUVbodystyle_Sedanbodystyle_Wagonbodystyle_nancat_x0cat_x1cat_x2fuel_type_Electricfuel_type_Flexiblefuel_type_Gasolinefuel_type_Hybrid
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Duplicate rows

Most frequently occurring

msrpyearmileagestock_typemodel_hashed_0model_hashed_1model_hashed_2model_hashed_3model_hashed_4model_hashed_5model_hashed_6model_hashed_7model_hashed_8model_hashed_9model_hashed_10model_hashed_11model_hashed_12model_hashed_13model_hashed_14model_hashed_15model_hashed_16model_hashed_17model_hashed_18model_hashed_19model_hashed_20model_hashed_21model_hashed_22model_hashed_23model_hashed_24model_hashed_25model_hashed_26model_hashed_27model_hashed_28model_hashed_29model_hashed_30model_hashed_31model_hashed_32model_hashed_33model_hashed_34model_hashed_35model_hashed_36model_hashed_37model_hashed_38model_hashed_39model_hashed_40model_hashed_41model_hashed_42model_hashed_43model_hashed_44model_hashed_45model_hashed_46model_hashed_47model_hashed_48model_hashed_49model_hashed_50model_hashed_51exterior_color_x0exterior_color_x1exterior_color_x2exterior_color_x3exterior_color_x4interior_color_x0interior_color_x1interior_color_x2interior_color_x3interior_color_x4drivetrain_All-wheel Drivedrivetrain_Front-wheel Drivedrivetrain_Rear-wheel Drivemake_Acuramake_Audimake_BMWmake_Buickmake_Cadillacmake_Chevroletmake_Dodgemake_Fordmake_GMCmake_Hondamake_Hyundaimake_INFINITImake_Jeepmake_Kiamake_Land Rovermake_Lexusmake_Lincolnmake_Mazdamake_Mercedes-Benzmake_Nissanmake_Porschemake_RAMmake_Subarumake_Toyotamake_Volkswagenmake_Volvobodystyle_Cargo Vanbodystyle_Convertiblebodystyle_Coupebodystyle_Hatchbackbodystyle_Minivanbodystyle_Passenger Vanbodystyle_Pickup Truckbodystyle_SUVbodystyle_Sedanbodystyle_Wagonbodystyle_nancat_x0cat_x1cat_x2fuel_type_Electricfuel_type_Flexiblefuel_type_Gasolinefuel_type_Hybrid# duplicates
10418670.3364292010.030863.00.00.00.00.0-1.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0-0.1203630.7514850.935431-0.592282-0.530580-0.7141820.7390521.171816-0.371113-0.0295200.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.123281-0.3816050.7437330.00.01.00.06
220345009.3580952020.041725.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0-1.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0-0.531811-0.0292671.072996-0.207851-0.945793-0.4942650.3875350.5897870.338319-0.4987021.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.01.016687-0.8669671.4716610.00.01.00.06
250649081.6166672020.031150.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0-1.00.00.0-0.5708810.3494540.686546-0.185022-0.751815-0.3139530.5480130.3238790.080760-0.1945091.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.830566-0.7890871.4474630.00.01.00.06
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